measuring firm internationalization using google trends

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LET ME GOOGLE THAT FOR YOU –

MEASURING FIRM INTERNATIONALIZATION USING

GOOGLE TRENDS

Harald Puhr

Assistant Professor

University of Innsbruck (UIBK)

Email: [email protected]

Jakob Müllner

Associate Professor

Vienna University of Economics and Business (WU)

[email protected]

Working paper draft, July 2021

Do not share without authors’ consent!

ABSTRACT

In this research note, we propose Google Trends as a uniquely versatile and overlooked resource for

International Business. Other disciplines like epidemiology, finance, and political science have

successfully used measures based on Google search volumes. Google Trends offers highly reliable and

finely grained data that provide a market-side complement to accounting-based measures of firm

internationalization in IB. First, we test the reliability of the Google Trends measures as substitute for

traditional measures of firm internationalization. Subsequently, we discuss conceptual and theoretical

properties of such a Google Trends internationalization measures. Finally, we propose new and

innovative applications of Google Trends internationalization measures in IB and beyond.

Keywords: Firm Internationalization, Google Trends, Measuring Internationalization, Degree of

Internationalization, Volume of Internationalization.

mailto:[email protected]

mailto:[email protected]

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INTRODUCTION

The process of firm internationalization its antecedents, consequences, and challenges are the founding

topics and raison d'etre of the discipline of International Business (IB). As IB scholarship built its

theoretical foundations (Buckley, 1988, Dunning, 1980, Johanson & Vahlne, 1977, Rugman, 1980),

decades of empirical studies have analyzed the process of internationalization using the degree of

internationalization and its changes as a dependent variable or applied it as an independent variable to

study the outcomes of internationalization (Welch, Nummela, & Liesch, 2016). Even where the

internationalization process was not at the center of research attention, the unique risks and

uncertainties of international business (Clarke & Liesch, 2017), inevitably require IB researchers to

control for a firm’s degree of internationalization when studying firm-level strategies. As such, the

measure of degree of internationalization permeates quantitative empirical IB research. Despite the

widely acknowledged weaknesses of traditional measures of firm internationalization (Marshall,

Brouthers, & Keig, 2020), IB research has produced very little advances in measurement of this

central construct.

The lack of innovation in measuring degree of internationalization, is particularly surprising since data

is becoming not only more available through digitalization, but digitalization itself is becoming a

dominating force in a firm’s internationalization process (Coviello, Kano, & Liesch, 2017), uncaptured

by traditional measures. Our research note aims at introducing Google Trends as a uniquely versatile

and rich source of data for IB research questions in general, and for measuring firms’ degree of

internationalization in particular. The global dominance of Google as an every-day search engine

(Figure A1) with a market share of 93% allows researchers to reach almost 7 billion people and gather

valuable data on their socio-economic behavior (globalstats, 2021). As such, Google Trends provides a

uniquely broad survey instrument to researchers in a variety of disciplines. For IB scholarship, the

detailed geographic segmentation of Google data allows for a novel, but distinct, measure of a firm’s

degree of internationalization.

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We propose several measures for firm internationalization using the globaltrends R package as a

toolbox for IB researchers1. Google Trends measures for internationalization can be used as a

supplement to corroborate results from traditional, accounting-based measures. Yet, we want to

highlight that our proposed Google Trends measures are not mere robustness checks for established

measures. We argue that they capture another, novel type of firm internationalization that is based on

consumer recognition. This different conceptualization offers new insights into additional dimensions

of internationalization in IB research. The proposed measures therefore answer calls for data

triangulation (Nielsen, Welch, Chidlow, Miller, Aguzzoli, Gardner, Karafyllia, & Pegoraro, 2020) to

enhance the methodological soundness of measures for internationalization (Verbeke & Forootan,

2012, Verbeke, Li, & Goerzen, 2009). Furthermore and because of the granularity of Google search

data, these measures open new research questions and methodologies for IB, which previously have

been difficult to study. Moreover, building measures for internationalization on a free and universally

available data source such as Google Trends, adds to greater reproducibility, replicability, and

transparency in IB research (Aguinis, Cascio, & Ramani, 2017, Beugelsdijk, van Witteloostuijn, &

Meyer, 2020). We first provide an overview of the historical use of Google Trends in science. Next,

we present Google Trends measures for internationalization that we computed with the globaltrends R

package (Puhr, 2021).

EXISTING APPLICATIONS OF GOOGLE TRENDS IN SCIENCE

Since 2010, Thompson Reuters Web of Science and PubMed jointly recorded about 900 papers that

used Google Trends or discussed its use in science. Figures A2 in the appendix shows the growth of

Google Trends as data source in academia. What is remarkable is the diversity of disciplines Google

Trends has been used in. Table A1 breaks down Google Trends-related publications in disciplines. The

overwhelming majority of papers and the earliest applications of Google Trends are from the field of

public health and particularly epidemiology (545). There Google search behavior has been used to

study patient behavior/symptoms and to track the spread of viral pandemics (e.g., Covid-19, Ebola,

and Flu) (Nuti, Wayda, Ranasinghe, Wang, Dreyer, Chen, & Murugiah, 2014). Shortly after public

health professionals recognized the value of Global Trends as a rich data source, economists joined the

1 The package was developed by Puhr (2021).

https://github.com/ha-pu/globaltrends

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bandwagon and started to use search volume data to predict macro-economic variables2 like private

consumption (Vosen & Schmidt, 2011), trade (Ma & Fang, 2021), unemployment, inflation, and

exchange rates.

Scholars also use Google Trends to analyze micro-economic aspects, such as stock prices and their

volatilities (Huang, Rojas, & Convery, 2020, Petropoulos, Siakoulis, Stavroulakis, Lazaris, &

Vlachogiannakis, 2021, Vlastakis & Markellos, 2012) and trading behavior (Preis, Moat, & Stanley,

2013). Recently, Cziraki, Mondria, and Wu (2021) have used data from Google Trends as a measure

of stock market investor attention3. Here, Google Trends has proven valuable as a forward-looking

market side measure of expectations with high predictive quality. Other research applies Google

Trends to traditional social science topics like environmental awareness, gender attitudes (Corbi &

Picchetti, 2020), religiosity, sexual behavior, suicide, or crime. During the COVID 19 pandemic,

Brodeur, Clark, Fleche, and Powdthavee (2021) studied social wellbeing using Google Trends data. In

tourism, Google Trends is used to predict travel activity (Bangwayo-Skeete & Skeete, 2015, Law, Li,

Fong, & Han, 2019, Önder, 2017, Yang, Pan, Evans, & Lv, 2015). In political science, scholars use

Google Trends for polling (Mavragani & Tsagarakis, 2016) and to measure issue salience (Mellon,

2013) and public opinion (Gruszczynski, 2019). Most prominently, Google Trends is used to create

indices of socio-economic uncertainty (Bontempi, Frigeri, Golinelli, & Squadrani, 2021). In sports

research (and practice) Google Trends is used to measure player/team performance/value (Fortuna,

Maturo, & Di Battista, 2018, Gift, 2020) and in meteorology researchers use Google Trends to study

extreme weather phenomena (Kam, Stowers, & Kim, 2019, Sherman-Morris, Senkbeil, & Carver,

2011)4.

Only in 2015, business scholars picked up on Google Trends borrowing from predictive methods in

economics and financial economics. Early applications used Google Trends to study technology

2 For an overview discussion see Choi and Varian (2012) with the most elaborate and most cited discussion of

Google Trends predictive qualities. 3 One study even uses Google Trends to forecast building permits (Coble & Pincheira, 2021). 4 In Figure A1, we create word clouds of keywords and keyword pairs from the sample of 855 papers across

disciplines that emphasize keywords based on their frequencies. From the word clouds several prominent

applications and themes of Google Trends, like forecasting, epidemiology, information seeking behavior, or

specific diseases emerge. Most strikingly, the word clouds reveal a complete absence of any micro-level or

business-related topics.

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adoption (Jun, Yeom, & Son, 2014) or advertising (Du, Hu, & Damangir, 2015, Hu, Du, & Damangir,

2014). To date, we are not aware of an application of Google Trends data in the fields of strategy or

international business5. In a recent working paper6, we use the Google Trends data to study the

moderating effect of internationalization on the sensitivity of stock prices during the Covid-19

pandemic. The results of the study highlight that Google Trends measures of firm internationalization

yield similar results as traditional internationalization measures used in IB.

TRADITIONAL APPROACHES TO MEASURING FIRM INTERNATIONALIZATION

Sullivan (1994) and later Marshall, Brouthers, and Keig (2020) provide an overview of traditional

approaches to measuring firm internationalization and categorize them into three groups: performance-

related measures, structural measures, and attitudinal measures. The first group focuses on costs and

revenues and includes measures like foreign sales as a percentage of total sales (FSTS), research and

development intensity (RDI), advertising intensity (AI), export sales as a percentage of total sales

(ESTS), and foreign profits as a percentage of total profit (FPTP). Structural measures are derived

from the global configuration of firm resources or assets and include measures like foreign assets as a

percentage of total assets (FATA) and overseas subsidiaries as a percentage of total subsidiaries

(OSTS). Finally, attitudinal measures focus on companies’ mindset and include top managers'

international experience (TMIE) and psychic dispersion of international operations (PDIO).

Despite their merits, these traditional measures have several limitations (Marshall, Brouthers, & Keig,

2020). First, they are country centric, distinguishing only between domestic and foreign. In other

words, the measures do not capture in how many and which countries the firm operates, or how

diverse this set of countries is. Additionally, the measures are very sensitive to home-country size.

More sophisticated measures of internationalization use country-level data and calculate the

distribution of sales, assets, and employees across countries using dispersion indices such as the Gini

coefficient, the Herfindahl index, or Entropy. Thereby, these indices capture the distribution of sales,

assets, or employees across individual country markets.

5 Dorobantu and Mullner (2019) use Google Trends data to capture country-level financial market uncertainty in

a robustness check. 6 Working paper available on request.

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Most recently, Marshall, Brouthers, and Keig (2020) proposed the RIMS measure of firm

internationalization that captures the “average depth of penetration across the breadth of all the

markets for the rest of the world excluding the firm’s primary market and then compares this to the

depth of penetration within the firm’s primary market”. RIMS is the most sophisticated measure of

market penetration and captures “how successfully a firm has penetrated the markets of the rest of-the-

world relative to its most successfully penetrated, primary market”. Though distribution-based

measures and RIMS, in particular, have superior conceptual validity, detailed data on companies’ sales

in individual countries is difficult to obtain. Even if available, such accounting data is commonly only

available on a yearly basis.

Scholars in other disciplines use Google Trends to measure how ideologies, diseases, or sentiments

have penetrated geographic locations. As such, Google Trends lends itself equally to study firm

internationalization. In the following, we apply the globaltrends R package (Puhr, 2021) to capture

Google search volumes and propose two distinct measures for internationalization. Then, we discuss

the conceptual particularities of these Google Trends measures, compared to existing accounting-

based measures.

APPLYING GOOGLE TRENDS TO MEASURE FIRM INTERNATIONALIZATION

We use the globaltrends R package (Puhr, 2021) to compute two conceptually distinct measures of

firm internationalization capturing (a) the volume of internationalization (VOI, comparable to net FDI

for a given period) and (b) the degree of internationalization (DOI, comparable to distribution-based

accounting measures). While VOI refers to the volume of global interest for the keyword, DOI

captures how evenly distributed this interest is across countries. As such, the two measures capture

two theoretically different aspects of internationalization. Figure 1 illustrates the distinction in a two-

by-two matrix and an accompanying numerical example of four foreign markets. Based on that, we

can derive four extreme archetypes of internationalized firms. A domestic giant has a large global

volume (VOI)7 but is only present in one market. A comparable global giant of equal volume (VOI)

may be evenly internationalized across all four markets (VOI). Using the Google Trends measures,

7 The Google Trends VOI measure is relative to the search volume of the reference terms. A VOI of 0.4 would

indicate the firm achieves 40% of the volume of the highly searched reference terms.

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this amounts to a DOI of 1. The same distinction can be made for search terms with low volume, or, in

our case firms of low VOI. Small firms may have very low global recognition (VOI). But they can

differ in terms of the distribution of this recognition: it can be concentrated in a single market (DOI =

0) or evenly spread across all markets (DOI = 1).

In Figure 2, we illustrate this differentiation and analyze the internationalization of a product with a

very distinctive, fad-like internationalization history, the fidget spinner toy. The fidget spinner was

invented and commercialized in late 2016 and became the world’s most sold toy within only two

months of commercialization (Economist, 2017, Rashid, 2017)8. After 2017, interest in the fidget

spinner somewhat resided, but – and this is the important aspect here – remained highly globalized. In

other words, the fidget spinner internationalized at a very fast pace and interest in the product is still

evenly distributed across the globe, but this interest remains at a much lower overall volume. Because

firms commonly do not exhibit such extreme internationalization patterns or growth this distinction

has not been prominent in IB research. Usually, studies simply control for firm size to capture volume

effects.

Google Trends data is extremely fine grained and offers time series for every firm at a country (even

district) level. While accounting-based measures by default capture internationalization only above a

certain threshold. Google Trends has no such reporting limitations and correctly identifies the fidget

spinner as a product of global interest, even if the absolute volume is very low. Thus, using

internationalization measures obtained from Google Trends offer a conceptually and empirically clean

distinction between degree of internationalization and volume of internationalization. This opens up

new avenues of theorizing and empirical testing (e.g. interactions between the two constructs).

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Insert Figure 1 & 2 about here

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Using the globaltrends R package (Puhr, 2021), we create a tailor-made operationalization of firm

internationalization that does not approximate the configuration of the firm’s international operations

but relies on their global recognition. The globaltrends package allows users to download time series

8 In Figure A4, in the appendix we illustrate the pace at which the fidget spinner toy internationalized across the

Atlantic.

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of Google search volumes from 2004 onwards. Because Google Trends organizes its data output as

single-country keyword batches, the package uses batched downloads. Within these batches, Google

Trends normalizes search volumes to values between 0 and 100.

The package includes a mapping algorithm to transform Google Trends output to a more general data

structure. For each country, users can select a set of baseline keywords that captures “standard” search

volumes in the country. These baseline keywords serve as reference points for all DOI and VOI

measures on search queries. The use of high-frequency baseline keywords ensures comparability

across keywords (e.g., firms) and time. All VOI and DOI measures can be interpreted relative to the

reference keywords. A DOI of .3 would indicate the search term, or company, achieves an inverted

Gini coefficent (international distribution) of 30% relative to the baseline keywords.

In the analysis of this research note, we select prominent search terms like “Gmail”, “Maps”,

“Translate”, “Wikipedia”, and “Youtube”. Because the globaltrends package allows users to alter the

sets of baseline keywords, it provides a built-in check the robustness of measures downloaded.

Researchers can compare measures across different sets of baseline keywords and ensure that

measures are not biased from the selection of the reference.

We then download batches of company names, including synonyms and alternative spellings, for each

country. For these search volumes, we compute season-adjusted and trend-only time series that serve

as robustness checks (Bokelmann & Lessmann, 2019)9. Next, we follow Castelnuovo and Tran (2017)

to map search volumes for firms to search volumes for baseline keywords in each country. We then

use these time series to compute search scores as the ratio between the search volumes for firm i in

comparison to search volumes for baseline keywords b1 to b5 in each country c:

𝑠𝑒𝑎𝑟𝑐ℎ 𝑠𝑐𝑜𝑟𝑒𝑖,𝑐 =𝑠𝑒𝑎𝑟𝑐ℎ 𝑣𝑜𝑙𝑢𝑚𝑒𝑖,𝑐

∑ 𝑠𝑒𝑎𝑟𝑐ℎ 𝑣𝑜𝑙𝑢𝑚𝑒𝑏,𝑐𝑏5𝑏=𝑏1

We can interpret search scores as the proportion of search volumes for company i compared to search

volumes for baseline keywords b1 to b5 within country c. Search scores therefore allow comparison

9 Figure A5 in the appendix shows seasonality in search volumes from Google Trends using the example of

Amazon with peaks around November (Black Friday) and December (Christmas).

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across companies, dates, and countries and provide insights into the local relevance of companies and

the exposure of these companies to the respective countries. For the example of Coca-Cola, Figure A6

in the appendix shows a strong overlap of search scores for all three search score time series, pointing

to the robustness of the Google Trends DOI measures.

Volume of Internationalization (VOI)

Country search scores focus on search volumes for companies in single countries. To compute a

company’s volume of internationalization, we focus on global search volumes instead of country

search volumes. Using the same approach as outlined above, we first download global search volumes

for each baseline keyword b1 to b5 and firm i. Next, we map the time series and compute global

search scores as the ratio of global search volumes for firm i and global baseline search volumes b1 to

b5.

Like country search scores, we can interpret global search scores as the proportion of global search

volumes for company i compared to global search volumes for baseline keywords b1 to b5. This

allows researchers to track changes in global interest in companies and highlights phases of fast or

receding internationalization. Since the computation of country and global search scores follows the

same procedures, the two measures have the same properties. This provides for a direct comparison

between country search scores and volume of internationalization in terms of global search scores.

Figure A4 in the appendix, compares across and within country search scores for the fidget spinner

and highlights differences in its internationalization across the globe.

Degree of Internationalization (DOI)

We conceptualize degree of internationalization based on the global dispersion of country search

scores for a search term or company. The more uniform the distribution of search scores across

countries, the higher a firm’s degree of internationalization. When the distribution of country search

scores is highly skewed, with high search scores in the firm’s home country and low search scores in

other countries, the firm has a low degree of internationalization. To compute a firm’s degree of

internationalization, we use the inverted Gini coefficient (Fisch, 2011),

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𝐷𝑂𝐼 𝐺𝑖𝑛𝑖𝑖 = 1 −∑ ∑ |𝑠𝑒𝑎𝑟𝑐ℎ 𝑠𝑐𝑜𝑟𝑒𝑖,𝑐−𝑠𝑒𝑎𝑟𝑐ℎ 𝑠𝑐𝑜𝑟𝑒𝑖,𝑑|𝑛

𝑑=1𝑛𝑐=1

2𝑛 ∑ 𝑠𝑒𝑎𝑟𝑐ℎ 𝑠𝑐𝑜𝑟𝑒𝑖,𝑐1𝑛𝑐

,

where search scorei,c and search scorei,d are the search scores for firm i across all country pairs c and

d. The globaltrends R package (Puhr, 2021), additionally measures the degree of internationalization

as inverted Herfindahl index (Bühner, 1987) and inverted Entropy (Hitt, Hoskisson, & Kim, 1997) of

the search score distribution. These additional measures serve as possible robustness checks to validate

the degree of internationalization computation. As shown in Figure A7 in the appendix, for Coca-Cola

all three approaches to compute degree of internationalization come to similar results, underlining the

robustness of the Google Trends DOI measures.

In Figure 3, we illustrate Google Trends DOI for three highly internationalized companies in the S&P

500 and for three counterparts of low degree of internationalization. Among the most internationalized

companies are Microsoft, Facebook, and Coca-Cola with average DOI ranging between 0.6 and 0.7.

Boxplots show that the limited range of values for companies over time supports the reliability of the

measures. For example, the bulk of monthly DOI values for Coca-Cola Company ranged from 0.5 to

0.7 between 2010 and 2020. This tight distribution shows that results are largely unaffected by fads in

Google search behavior (for both the company and the baseline keywords). On the lower end of the

internationalization scale in S&P 500, we find the companies Alaska Air Group, Illinois Tool Works,

and J.M. Smucker Company. Alaska Air Group, for example, achieves a DOI between 0.02 and 0.07.

Because we map Google Trends data to the same scale for all observations, the degree of

internationalization is comparable across the entire sample. Therefore, we can show that on average,

Microsoft is roughly 30 times more international than Alaska Air. Figure 4 shows the

internationalization history of the same six companies between 2010 and 2020. There is no common

trend observable, indicating that search volumes for the baseline keywords do not influence time series

systematically. In sum, the company comparisons in Figure 4 as well as the internationalization time

series in Figure 5 underscore the high validity of the Google Trends DOI measure.

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Insert Figures 3 & 4 about here

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Finally, in Figure 5, we plot monthly volume of internationalization (VOI) and degree of

internationalization (DOI) values for firms in the S&P 500. The plot highlights the related but yet

distinguishable nature of VOI and DOI for larger companies. Here several large companies exhibit

high volumes of global search activity but relatively low degree of internationalization (i.e.,

distribution of search activity across countries).

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Insert Figure 5 about here

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EMPIRICAL TESTS OF RELIABILITY AS A MEASURE FOR FIRM

INTERNATIONALIZATION

We create several types of empirical tests to analyze systematic differences between Google Trends

measures and traditional measures for internationalization. While these tests implicitly check the

reliability of Google Trends measures as a substitute for traditional measures, it is important to stress

that the proposed measures are not intended or conceptualized as a replacement for existing measures.

Rather, we insist, they contribute a conceptually and theoretically different perspective on firm

internationalization. In this sense, the differences of Google Trends measures in comparison to

traditional measures provide the novelty and primary contribution of the measures. Also, it is

important that such differences are not interpreted as biases compared to “true” firm

internationalization. This would assume (a) that traditional measures are unbiased and (b) that there

only exists one true type of and degree of internationalization. Instead, the following robustness

checks assess if Google Trends DOI measures can serve as an indistinguishable substitute for

traditional DOI measures.

In a first step, we analyze the correlation of the Google Trends DOI measure with three existing,

widely accepted datasets and measures. We first select S&P 500 as a sample because of the detailed

data availability. We extract geographical sales from the firms’ annual reports filed with the Securities

and Exchange Commission (SEC) and use it to compute share of foreign sales, assets, and profits. In

addition, we use Orbis Bureau van Dijk to compute the share of foreign subsidiaries for S&P 500

firms. Despite excellent data availability, S&P 500 varying degrees of geographical reporting among

S&P 500 companies do not allow for a more fine-grained geographical distinction of

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internationalization measures. Also, S&P 500 includes only US companies and does not allow to test

for home-country biases.

To address these shortcomings in using S&P 500 accounting data to test the reliability of the Google

Trends DOI measure, we obtain two existing datasets that use country-level granular data for their

internationalization measures. First, UNCTAD annually published the Transnational Corporations

Index (TNCI) for the largest 100 companies in the world. Second, Marshall, Brouthers, and Keig

(2020) provide the detailed and comprehensive RIMS measure of firm internationalization for an

international sample of 484 MNEs. Both datasets were kindly made available by authors for

comparison with the Google Trends DOI. To complement these traditional measures of DOI, we also

use number of foreign countries and number of foreign subsidiaries to total number of subsidiaries

from Bureau van Dijk Orbis for comparison. In Table 1, we provide descriptive statistics for the

Google Trends VOI and DOI measures and comparison datasets. In Table 2, we show correlations of

the Google Trends DOI measures with traditional measures.

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Insert Tables 1 & 2 about here

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VOI measures are show a low correlation with DOI measures, achieving a maximum of 0.254 between

DOI Giniobserved and VOIobserved. This supports our argument that volume of internationalization and

degree of internationalization are positively correlated but separable constructs. DOI measures using

Gini coefficient and Herfindahl indices are highly homogenous achieving a bivariate correlation of

0.702. Entropy-based measures have very low, even negative correlation with other DOI measures

indicating that these measures might capture some other patterns of firm internationalization.

Comparing the Google Trends DOI measure to traditional DOI measures such as share of foreign

assets (r = 0.250), share of foreign sales (r = 0.378), and share of foreign profits (r = 0.134), shows

positive correlations. In addition, we also find a positive relation between the RIMS measure and the

Google Trends DOI measure (r = 0.364). Because the RIMS measure is the most sophisticated and

unbiased measure among traditional measures, the comparatively high correlation is a strong indicator

of the reliability of Google Trends measures. Although the positive correlation with UNCTAD’s TNCI

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is only 0.024, we consider these findings as support for the notion that the Google Trends DOI

measure captures distinct aspects of a firm’s degree of internationalization.

In a second step, we devise multivariate empirical robustness checks for the predictive quality of the

Google Trends DOI measure. We combine the three comparison datasets and their traditionally

measured degree of internationalization. To facilitate comparison, we standardize the traditional DOI

and create a dummy variable for each dataset (S&P 500, RIMS, TNCI) which we use to absorb biases

from differing datasets and measurement approaches. As an additional comparison, we also obtain

number of foreign countries and number of foreign subsidiaries to total number of subsidiaries from

Bureau van Dijk Orbis.

We run predictive regressions using the Google Trends DOI measure as dependent variable and

include traditional DOI as the independent variable10 (Table 3). In a perfectly collinear setting in

which the independent variable replicates Google Trends DOI, this would yield a highly significant

beta coefficient of 1 and a R2 converging to 1. We illustrate this perfect predictability scenario in

Model 1, in which we use Google Trends DOI as both dependent and independent variable. In Model

2, we replace Google Trends DOI with traditional DOI as the independent variable. Even after

controlling for idiosyncrasy related to the respective dataset and measurement, we find a positive

relation between traditional DOI and Google Trends DOI (0.259, p = 0.000). In addition, we replicate

the test with number of foreign countries (Model 3) and number of foreign subsidiaries to number of

total subsidiaries (Model 4) as alternative measures for internationalization. In both models, the

relation with Google Trends DOI remains robust. In sum, this test confirms our interpretation from the

bivariate correlations: The Google Trends DOI measures correlate significantly with traditional

measures and, I the absence of traditional measures, can be used as a proxy for traditional degree of

internationalization. Still, the non-explained variance (1-R2 = 85.2%) indicates that the Google Trends

DOI measure is not a mere replica of traditional measures.

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Insert Table 3 about here

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10 As indicated, we include dummy variables to mark the datasets and control for idiosyncratic measurement.

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There are two potential reasons for that. First, the Google Trends DOI captures aspects related to

internationalization but provides a conceptually different perspective. We strongly subscribe to this

interpretation and stress that, in fact, it is this conceptual difference that provides the most important

contribution of the Google Trends DOI measure to IB research. Rather than replicating existing

internationalization measures, it provides a conceptually different and novel perspective. The second

reason for imperfect predictive power could be systematic differences in the concepts of

internationalization. Such differences would only be relevant if scholars apply Google Trends DOI as

a substitute or proxy for traditional measures firm internationalization (as opposed to a novel and

unique type of internationalization as described in the next section). To provide guidance on the use of

Google Trends DOI measures, we devise tailored robustness checks for specific differences or unique

biases discussed in connection with data from Google Trends or DOI measures. We provide marginal

effect plots for the regressions showing the differences between Google Trends measures and

traditional measures of DOI in Figure 8 below and in Figure A8 in the appendix.

One common criticism of traditional DOI measures relates to home-country bias (Table 4).

Specifically, firms from smaller countries naturally achieve higher foreign-to-total ratios compared to

firms from larger countries Marshall, Brouthers, and Keig (2020). To test for home-country size bias,

we include home-country GDP in Model 5 and interact it with traditional DOI. The sign and

significance of the interaction term inform us about the direction and significance of home-country

size bias compared to traditional DOI. We observe that a negative interaction traditional DOI and GDP

(-0.034, p = 0.034), indicating that Google Trends DOI slightly overestimates the DOI of firms from

large home countries. In Model 6, we add a home-country development bias, measured as GDP/capita.

The statistically insignificant interaction term (0.053, p = 0.521) hints at the absence of a potential

bias.

In a third and final step of robustness checks, we focus on studying biases in Google data that were

discussed in related literature. These biases are related to the unique characteristics of the data source.

Given that the globaltrends R package (Puhr, 2021) uses internet search data from Google, the data

could inhibit an internet bias. We therefore introduce internet usage in the home country to control for

this bias (Model 7). The statistically insignificant interaction term (0.092, p = 0.506), however,

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indicates the absence of a bias. Furthermore, the language of Google users could distort

representatives of Google data. To test for home-country language bias, we fist add a measure of

English proficiency from EF (Model 8) and second add a dummy indicating whether the home country

uses English as an official language (Model 9). Negative interaction effects for English proficiency (-

0.185, p = 0.000) and English as an official language (-0.236, p = 0.002) suggest that an English

language bias increases Google Trends DOI estimates. However, we also observe that the main effect

of traditional DOI on Google Trends DOI remains robust across all models.

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Another potential source of bias could relate to how firms of different industries and sizes are captured

in Google Trends data (Table 5). In this respect, an important concern relates to firm size. While firm

size and internationalization are naturally related, searches volumes on Google for smaller or larger

firms could be systematically lower. To assess the existence of such a systematic difference, we add

total sales (Model 10), number of employees (Model 11), and total assets (Model 12) as indicators for

firm size to our regressions. Based on the positive interactions in Model 10-12, we assume that Google

Trends DOI underestimates the degree of internationalization for large firms. Alternatively, these

findings could also suggest that Google Trends DOI is less perceptible to firm-size than traditional

DOI. We test whether focus on business or retail customers distorts the Google Trends DOI estimates.

To this end, we include a dummy variable for B2B firms, coded based on SIC industry classification.

The statistically insignificant interaction term in Model 13 (-0.097, p = 0.244) indicates that B2B does

not systematically result in differences between Google Trends DOI and traditional measures.

Another Google-related bias discussed in the literature regards the special cases of countries with

restrictions on internet usage, most prominently China and Russia. For firms with large operations in

these countries, the Google Trends DOI could underestimate degree of internationalization due to lack

of data availability. To control for this effect, we add China exposure, measured as number of

subsidiaries in China to number of total subsidiaries, to Model 14. The statistically insignificant

interaction (-0.022, p = 0.547) shows that the Google Trends DOI estimates are not systematically

16

distorted by greater exposure to China. As in the country-bias models, the main effect of traditional

DOI remained robust across all models.

-------------------------------


-------------------------------

We see three measurement-related reasons why, despite restrictions to the usage of Google, we do not

observe a bias from exposure to China in the Google Trends DOI. First, we compute Google Trends

DOI as the global dispersion of country search scores. Therefore, distorted search scores in one

country (e.g., in China due to restricted access to Google) result only in limited distortion to the

dispersion measure as a whole. Yet the degree of distortion to the dispersion measure also depends on

the overall search score pattern. If China is the firm’s only foreign market, underestimating search

scores for China can distort Google Trends DOI. On the other hand, if China is just one of several

foreign markets, inaccurate search scores for China do not affect Google Trends DOI. Second, we

compute search scores as search volumes for a firm relative to “standard” search volumes. Thus, low

search volumes for a specific firm do not translate into low search scores if “standard” search volumes

are equally low (e.g., due to restrictions). Third, despite restrictions, users still rely on Google as a

search engine. In China, for example, Google’s market share is 2.58% (Figure A9). Albeit this is far

below its global market share of 90%, data from Google Trends on search volumes in China still

represents 36 million Google users. In the appendix (Figure A10), we show search volumes for Apple

and Microsoft in China and Russia from Google Trends. Search volumes seem stable over time and

equally dispersed across the two countries. Given that the interest in Apple and Microsoft by the

limited number of users in China and Russia is not systematically different from the wider population,

we consider Google Trends as a reliable source of information for these two countries.

-------------------------------

Insert Figure 8 about here

-------------------------------

Based on our findings, we believe the Google Trends DOI measures offer a reliable operationalization

to complement traditional DOI. Still, we suggest precautions (e.g., country and industry fixed effects)

to account for potential differences in Google Trends DOI and to apply robustness checks using

17

traditional measures11. Most importantly, however, we consider the most valuable applications of

Google Trends DOI measures not in a novel proxy of traditional measures of degree of

internationalization, but as a novel, market-side conceptualization of internationalization in its own

right and theoretical merit.

CONCEPTUAL CONSIDERATIONS OF GOOGLE TRENDS INTERNATIONALIZATION

MEASURES

Our working paper on the moderating effect of firm internationalization on firm resilience to Covid-

19, shows that Google Trends internationalization measure can be used along traditional measures, as

a robustness test or primary measure, yielding identical results. However, it is important to stress that

the global distribution of search volumes captures a different, more market-related type of firm

internationalization. This approach may be less suitable in some contexts but opens other areas where

internationalization cannot be captured through traditional accounting measures. The proposed

measures, as such, do not neatly fit into the groups proposed by Sullivan (1994), they are neither

derived from companies’ transactions (performance), location of assets (structural), or mindset

(attitudinal). Rather, the Google Trends DOI measures constitute a fourth set of variables that capture

the footprint of the company in the global environment. When should IB researchers use Google

Trends measures instead of traditional accounting-based measures? In this section, we argue that the

choice of measure (traditional or Google) needs to be based on the theoretical mechanisms underlying

the hypothesized effects and showcase some theoretical mechanisms that lend themselves to the use of

Google Trends measures.

Traditional accounting-based measures are derived from companies’ global operations. As such, they

are superior when research questions address operational mechanisms like operational flexibility or

real options. In addition, such measures are better at capturing organizational complexity and cost of

coordination within a company. Finally, accounting-based measures will likely perform better when

arguments relate to the organizational structure of the company (diversification or internal capital

11 We show one such application in our working paper on stock prices during the Covid-19 pandemic.

18

markets). Table 6 highlights some of the differences between the Google Trends DOI measures and

traditional measures of internationalization.

-------------------------------


-------------------------------

The Google Trends measures proposed in this research note are not based on the global distribution of

assets or sales but the companies’ recognition in the eyes of customers/investors. As such, they do not

capture operational boundaries of firms but the firms’ footprint as in the global mindset of customers,

shareholders, and stakeholders. This makes the Google Trends DOI measures particularly useful for

IB studies that address mechanisms that are theoretically founded in market-based or market-side

mechanisms (as opposed to global market operation). In our above-mentioned working paper, for

example, we analyze the protective effect of firm internationalization on stock prices of S&P 500

companies during the Covid-19 pandemic. Because stock prices are strongly related to investors’

perceptions of and knowledge about the company, Google Trends measures are conceptually closer to

the stock market sell-off process that is studied. Along the same lines, Cziraki, Mondria, and Wu

(2021) study stock price reactions and use Google Trends to measure investor interest and information

asymmetry across countries.

In addition to stock returns, many IB phenomena are driven by similar market-side mechanisms. Most

evidently, international marketing provides a myriad of applications to test a company’s international

recognition, brand value etc. Signaling effects from reputation or popularity are also important

theoretical components of corporate social responsibility (CSR) research. Other very promising

applications in IB research that are related to non-market strategy. There, the global recognition of

firms will determine the degree to which it is politically instrumentalized by host-country politicians

and thus indicates the firms’ exposure to external opportunism and activism. The Google Trends DOI

measures, also reflect the diversity of a company’s stakeholder audience more accurately than

traditional measures of internationalization. For empirical applications where the mechanism under

investigation is based on market-side perception of a company (rather than physical operations), the

Google Trends DOI measures have higher validity than traditional accounting-based measures.

19

In addition to the theoretical mechanism studied, researchers should consider the temporal dimension

when selecting a Google Trends or accounting-based measure of internationalization. As indicated

before, there are conceptual and empirical differences between volume of internationalization and

degree of internationalization. These differences are particularly important when researchers study fast

and dynamic processes of internationalization. While accounting-based measures make it difficult to

dissect these two components, Google Trend measures provide comparable operationalization for

them. In addition, accounting based measures have very long time-lags as companies internationalize.

Google Trends measures capture the rapid internationalization of true born globals and fads, allowing

researchers to better study the dynamics of the internationalization process. This means that Google

Trends can be used even to measure the internationalization effects of intra-year events like market

entries, M&As, or IJVs. Yearly accounting measures, however, inevitably aggregate the effects of all

internationalization steps (both in terms of volume and degree) to a yearly cross-section.

STRENGTHS & WEAKNESSES OF GOOGLE TRENDS MEASURES

Above, we argue that Google Trends internationalization measures are not only additions to the

existing universe of measures but capture a slightly different concept of firm internationalization. In

the following, we discuss some of the advantages, disadvantages, and dangers related to the proposed

measures.

Admittedly, Google Trends measures may have some empirical disadvantages or dangers. The first,

and most obvious is keyword contamination. Search volumes for Apple Inc., for example, are likely

biased by Google queries on the fruit. Thus, double meanings may contaminate individual keywords.

This problem is less severe when the keywords are names and not generic terms. For this purpose, we

recommend prior testing on the Google Trends portal and robustness checks using dummy variables

for contaminated keywords or subsample analyses. A related problem can occur when companies are

associated with and thus searched under several names (e.g., Google & Alphabet or Volkswagen &

VW). Such keyword dilution can cause the volume and degree of internationalization for a single

company to be under- or overestimated.

Another problem with Google Trends measures results from language differences between countries.

When using keyword in English, search scores are less reliable in countries where English proficiency

20

is low. It is important to stress that such differences affect the degree of internationalization to a lower

extent because it is based on the comparison of search score distributions across countries. In other

words, the bias in different countries is the same for all companies. The volume of internationalization,

on the other hand, can be lower, if a company is mostly recognized in a language other than English

(e.g., Japanese and Chinese companies). To address such concerns, we recommend prior testing on the

Google Trends portal and robustness checks using subsamples of firms’ home countries or weigh

internationalization measures by each country’s English proficiency.

The third issue relates to time series normalization by Google Trends. To make search volumes for a

larger number of keywords comparable, scholars must choose up to five baseline keywords that serve

as reference. For applications related to the internationalization of companies, we recommend a

combination of baseline keywords with universal meaning and relevance (e.g., Gmail, Maps,

Translate, Wikipedia, YouTube). Thus, like the use of fixed effects in multivariate regressions, all

internationalization measures for the companies in the sample must be interpreted relative to the joint

internationalization of these baseline keywords. Ideally, the selection of good baseline keywords will

create unbiased and robust measures. However, if scholars select baseline keywords that are highly

volatile over time, uncommon or biased themselves (see point 1 and 2 before), all measures of

internationalization will be biased. Therefore, we recommend prior testing on the Google Trends

portal and robustness checks that address the problem using alternative baseline keywords.

Striking advantages in the proposed measures, compared to traditional accounting measures, outweigh

these weaknesses. The measures allow researchers data triangulation (Nielsen, Welch, Chidlow,

Miller, Aguzzoli, Gardner, Karafyllia, & Pegoraro, 2020) to overcome issues in the measurement of

internationalization (Verbeke & Forootan, 2012, Verbeke, Li, & Goerzen, 2009). Therefore, scholars

can use Google Trends measures to operationalize degree of internationalization independent of type

of value chain activity, entry mode choice, and strategic motives for internationalization. Moreover,

researchers can separate the dispersion of international operations (i.e., degree of internationalization)

from the intensity of international operations (i.e., volume of internationalization).

Google Trends offers exponentially higher data granularity, availability, and accuracy. Data is

collected and published daily and can be downloaded for any company or keyword. As such, Google

21

Trends measures can be applied to samples where data availability is problematic, where intra-year

data is required and where existing measures are unreliable. These advantages make Google Trends

data extremely versatile and opens new perspectives on firm internationalization, as outlined above,

and offers entirely new, IB relevant applications that go beyond the concept of firm

internationalization. This gives scholars the opportunity to investigate the internationalization of

cultural values, consumer behavior, or political trends (Aguzzoli, Gardner, & Newburry, 2021). Thus,

the proposed measures provide an approach to cope with the growing multiplicity and dynamism of

the global business system (Eden & Nielsen, 2020). We discuss some of these opportunities below.

We believe that the application of Google Trends measures can also increase methodological rigor in

IB research. First, the usage of Google Trends contributes to the diversity and novelty of data sources

and methods in IB (Aguzzoli, Gardner, & Newburry, 2021, Miller, Welch, Chidlow, Nielsen,

Pegoraro, & Karafyllia, 2021). Thereby, applications of Google Trends data enhance the quality and

diversity of research methods in the IB community. Second, Google Trends is freely and universally

available. As an open-source software package, globaltrends provides open access to a novel and rich

data source. Using the proposed measures to operationalize internationalization therefore enhances

reproducibility, replicability, and transparency in IB research (Aguinis, Cascio, & Ramani, 2017,

Beugelsdijk, van Witteloostuijn, & Meyer, 2020).

NOVEL RESEARCH APPLICATIONS THROUGH GOOGLE TRENDS

INTERNATIONALIZATION MEASURES

While this research note focuses on the use of Google Trends data as an alternative, yet conceptually

slightly different measure of firms’ degree of internationalization, the potential contribution of the

package to IB scholarship goes far beyond this as it opens up a myriad of new research opportunities

that are unattainable with existing measures. In this section, we subjectively showcase some striking

potential applications of Google Trends data in IB research.

First, Google Trends data can be used to measure the degree of internationalization of non-traditional

entities, or even concepts. For example, the measures can be equally applied to products. For example,

scholars can track the internationalization of automotive models or electronic gadgets, as opposed to

companies (examples provided in Figure A11). Equally, Google Trends DOI measures can be applied

22

to individuals (e.g., academics, politicians, athletes, or managers; examples in Figure A12), non-

corporate organizations (e.g., universities, sports clubs, terror organizations, or NGOs; examples in

Figure A13), or even fashions, trends, and socio-economic phenomena (e.g., pandemics, hypes,

political developments, or sentiments; examples in Figure A14). Most strikingly, all these metrics are

from the same source, scale and therefore directly comparable. As such, for example, IB researchers

could study the international recognition of academic scholars, their universities and theoretical

contributions using the same metric.

Second, the high granularity of Google Trends data both in the temporal and geographic dimension

open entirely new applications of DOI. The high granularity of the data could be used to study the

dynamics of internationalization in much greater detail. This allows researchers to study rapid

internationalization processes (e.g., born globals) or the dynamics of individual steps of

internationalization (e.g., market entry). This IB allows researchers to compute abnormal returns of

internationalization and study the effects of varying internationalization strategies (e.g., M&A, IJVs)12.

For example, IB researchers can study the added DOI a firm acquires in an acquisition (upper panels

in Figure A15), or the internationalization effect of a market entry in one country using this country’s

Google Trends time series. Equally applying on the concept of abnormal DOI returns, the authors of

this note made attempts to measure the internationalization benefits for researchers receiving the

Nobel Price (third panel in Figure A15), or, on a less serious note, the internationalization benefits to

the soccer club Juventus Turin of the acquisition of Cristiano Ronaldo (lower Panel in Figure A15).

Google Trends data also have superior geographical availability opening new avenues of empirical

investigation. Google Trends data are available on a on a subnational or district level. As such, IB

researchers can follow firms’ exposure single countries, creating a measure of firm concentration

within a country (Figure A16). It is, for example, an interesting question for IB to study if firms of

varying degrees of dispersion within the US are affected differently by changes in in the global policy

environment. For example, the authors of this research note used companies’ dispersion within the US

12 Focusing on the first derivative of the time series, provides IB researchers with a novel measure for speed of

internationalization. The time series’ standard deviation can be interpreted as a volatility measure for

internationalization.

23

as a variable explaining abnormal stock returns of S&P 500 companies during the US presidential

election13.

Third, Google Trends data can be used to study patterns of internationalization. The proposed

measures for internationalization do not distinguish Google Trends data in a geographic sense. In other

words, countries are treated as categorical buckets without consideration of geographical location or

colocation. Including such geographical data, would allow IB researchers to map firms’ regional

imprints potentially contributing to the regionalization literature in IB (Rugman & Verbeke, 2004).

Like the entropy measures, these footprints could be applied to firm dyads, assessing the structural

similarity of firms. Studying how many markets different firms share, and how theses primary markets

overlap would provide an indication for the structural similarity of firms14.

Finally, Google Trends data can be used by IB scholars (and beyond) to revisit some of the country-

level constructs used in IB. Similar to existing applications in the field of economics, IB scholars can

use series of search terms to study political risk in countries. One such preliminary attempt by the

authors of this research note is illustrated in Figure A17 in the appendix. To construct the country risk

index, the authors surveyed academic experts to construct a risk-related survey battery including 106

individual search terms. Subsequently, search volumes of these search terms in each country (and their

translated local language equivalents) were obtained with the globaltrends R package (Puhr, 2021).

Volumes of search terms were aggregated and indexed to yield a country risk score for each country.

While the index is still in its infancy, these first results are promising.

Such country-level measures can be applied to compare countries’ political risk but more importantly,

monthly Google Trends data is uniquely suitable to study more short-term changes in political risk

within a country. Equally, Google Trends data can be used to revisit concepts about country distance

often used in IB (e.g., psychic distance, cultural distance). Scholars could, for example, create a bucket

of search queries to represent consumers’ cultural, economic, and social profile. Comparing the

13 Working paper available on request. 14 In a recent application of this approach, authors studied the internationalization process of selected Crypto

Fintech startups and found striking similarities in their early internationalization process to Asia and the US.

24

correlations of such time series between countries would yield interesting and potentially new

measures of country distance.

CONCLUSION

Our research note uses the globaltrends R package and Google Trends to introduce two novel and

uniquely versatile measure of firm internationalization based on Google search volumes. More

specifically, we propose Google Trends data for measuring firms’ volume of internationalization and

degree of internationalization. The measures can be used as more readily available substitutes for

traditional, accounting-based measures as illustrated in our working paper on stock prices during the

Covid-19 pandemic.

However, we also argue that these measures are not mere substitutes but complement the universe of

measures available, by capturing a market-side internationalization or exposure of firms. As such, the

Google Trends DOI measures are more suitable to capture theoretical mechanisms of market-side

exposure or signaling processes in international business than accounting-based measures which

capture the internationalization of companies’ operations but not necessarily their recognition across

the globe. In addition, we argue that the responsive nature of Google Trends time series allows

scholars to capture the dynamics of rapid internationalization and de-internationalization processes

more reliably than with accounting-based measures.

In addition to introducing and discussing the two measures proposed, we highlight the unique

versatility of Google Trends measures for IB scholarship and beyond. We propose additional potential

applications of Google Trends data to measure firms’ concentration within countries. Applying the

Google Trends DOI measures beyond firm internationalization, IB researchers can study the

internationalization of products, persons, ideas, events, fads, or scandals, even academic authors and

papers. In addition, the data granularity, availability, and accuracy of Google Trends opens entirely

new methodologies for IB researchers, such as event study approaches or time series analysis. We

believe that, due to its free and universal availability, the usage of Google Trends as a data source will

enhance methodological rigor and diversity in IB research. Finally, the scalability and coverage of

25

Google as a survey instrument in virtually every country of the world, endows IB scholars to revisit

measurement of core IB concepts such as country risk and, most importantly, distance.

26

REFERENCES

Aguinis, H., Cascio, W. F., & Ramani, R. S. 2017. Science’s reproducibility and replicability crisis:

International business is not immune. Journal of International Business Studies, 48(6): 653-63.

Aguzzoli, R., Gardner, E., & Newburry, W. 2021. Letter from the Editors: Special Forum on

“Research Methods in International Business.”. AIB Insights, 21(1).

Bangwayo-Skeete, P. F. & Skeete, R. W. 2015. Can Google data improve the forecasting performance

of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46: 454-64.

Beugelsdijk, S., van Witteloostuijn, A., & Meyer, K. E. 2020. A new approach to data access and

research transparency (DART). Journal of International Business Studies, 51(6): 887-905.

Bokelmann, B. & Lessmann, S. 2019. Spurious patterns in Google Trends data - An analysis of the

effects on tourism demand forecasting in Germany. Tourism Management, 75: 1-12.

Bontempi, M. E., Frigeri, M., Golinelli, R., & Squadrani, M. 2021. EURQ: A new web search-based

uncertainty index. Economica.

Brodeur, A., Clark, A. E., Fleche, S., & Powdthavee, N. 2021. COVID-19, lockdowns and well-being:

Evidence from Google Trends. Journal of Public Economics, 193.

Buckley, P. J. 1988. The limits of explanation: Testing the internalization theory of the multinationial

enterprise. Journal of International Business Studies, 19(2): 181-93.

Bühner, R. 1987. Assessing international diversification of West German corporations. Strategic

Management Journal, 8(1): 25-37.

Castelnuovo, E. & Tran, T. D. 2017. Google it up! A Google Trends-based uncertainty index for the

United States and Australia. Economics Letters, 161: 149-53.

Choi, H. Y. & Varian, H. 2012. Predicting the present with Google Trends. Economic Record, 88: 2-9.

Clarke, J. E. & Liesch, P. W. 2017. Wait-and-see strategy: Risk management in the

internationalization process model. Journal of International Business Studies, 48(8): 923-40.

Coble, D. & Pincheira, P. 2021. Forecasting building permits with Google Trends. Empirical

Economics.

Corbi, R. & Picchetti, P. 2020. The cost of gendered attitudes on a female candidate: Evidence from

Google Trends. Economics Letters, 196.

Coviello, N., Kano, L., & Liesch, P. W. 2017. Adapting the Uppsala model to a modern world: Macro-

context and microfoundations. Journal of International Business Studies, 48(9): 1151-64.

Cziraki, P., Mondria, J., & Wu, T. 2021. Asymmetric attention and stock returns. Management

Science, 67(1): 48-71.

Dorobantu, S. & Mullner, J. 2019. Debt-side governance and the geography of project finance

syndicates. Journal of Corporate Finance, 57: 161-79.

Du, R. Y., Hu, Y., & Damangir, S. 2015. Leveraging trends in online searches for product features in

market response modeling. Journal of Marketing, 79(1): 29-43.

Dunning, J. H. 1980. Toward an eclectic theory of international production: Some empirical tests.

Journal of International Business Studies, 11(1): 9-31.

Economist. 2017. The lessons of fidget spinner: Fidget revolution, The Economist. London.

Eden, L. & Nielsen, B. B. 2020. Research methods in international business: The challenge of

complexity. Journal of International Business Studies, 51(9): 1609-20.

Fisch, J. H. 2011. Information cost and internationalization performance. Global Strategy Journal,

2(4): 296-312.

27

Fortuna, F., Maturo, F., & Di Battista, T. 2018. Clustering functional data streams: Unsupervised

classification of soccer top players based on Google Trends. Quality and Reliability

Engineering International, 34(7): 1448-60.

Gift, P. 2020. Moving the needle in MMA: On the marginal revenue product of UFC fighters. Journal

of Sports Economics, 21(2): 176-209.

Gruszczynski, M. 2019. Evidence of partisan agenda fragmentation in the American public, 1959–

2015. Public Opinion Quarterly, 83(4): 749-81.

Hitt, M. A., Hoskisson, R. E., & Kim, H. 1997. International diversification: Effects on innovation and

firm performance in product-diversified firms. Academy of Management Journal, 40(4): 767-98.

Hu, Y., Du, R. Y., & Damangir, S. 2014. Decomposing the impact of advertising: Augmenting sales

with online search data. Journal of Marketing Research, 51(3): 300-19.

Huang, M. L. Y., Rojas, R. R., & Convery, P. D. 2020. Forecasting stock market movements using

Google Trend searches. Empirical Economics, 59(6): 2821-39.

Johanson, J. & Vahlne, J.-E. 1977. The internationalization process of the firm – A model of

knowledge development and increasing foreign market commitments. Journal of International

Business Studies, 8(1): 23-32.

Jun, S.-P., Yeom, J., & Son, J.-K. 2014. A study of the method using search traffic to analyze new

technology adoption. Technological Forecasting and Social Change, 81: 82-95.

Kam, J., Stowers, K., & Kim, S. 2019. Monitoring of drought awareness from Google Trends: A case

study of the 2011–17 California drought. Weather, Climate, and Society, 11(2): 419-29.

Law, R., Li, G., Fong, D. K. C., & Han, X. 2019. Tourism demand forecasting: A deep learning

approach. Annals of Tourism Research, 75: 410-23.

Ma, S. Z. & Fang, C. 2021. The effect of online search on international trade. Applied Economics.

Marshall, V. B., Brouthers, L. E., & Keig, D. L. 2020. RIMS: A new approach to measuring firm

internationalization. Journal of International Business Studies, 51(7): 1133-41.

Mavragani, A. & Tsagarakis, K. P. 2016. YES or NO: Predicting the 2015 GReferendum results using

Google Trends. Technological Forecasting and Social Change, 109: 1-5.

Mellon, J. 2013. Where and when can we use Google Trends to measure issue salience? PS: Political

Science & Politics, 46(2): 280-90.

Miller, S. R., Welch, C., Chidlow, A., Nielsen, B. B., Pegoraro, D., & Karafyllia, M. 2021. The

adoption challenge: An analysis of research methods in JIBS. AIB Insights, 21(2). . AIB

Insights, 21(2).

Nielsen, B. B., Welch, C., Chidlow, A., Miller, S. R., Aguzzoli, R., Gardner, E., Karafyllia, M., &

Pegoraro, D. 2020. Fifty years of methodological trends in JIBS: Why future IB research needs

more triangulation. Journal of International Business Studies, 51(9): 1478-99.

Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S. S., Dreyer, R. P., Chen, S. I., & Murugiah, K. 2014.

The use of Google Trends in health care research: A systematic review. Plos One, 9(10).

Önder, I. 2017. Forecasting tourism demand with Google Trends: Accuracy comparison of countries

versus cities. International Journal of Tourism Research, 19(6): 648-60.

Petropoulos, A., Siakoulis, V., Stavroulakis, E., Lazaris, P., & Vlachogiannakis, N. 2021. Employing

Google Trends and deep learning in forecasting financial market turbulence. Journal of

Behavioral Finance.

Preis, T., Moat, H. S., & Stanley, H. E. 2013. Quantifying trading behavior in financial markets using

Google Trends. Scientific Reports, 3.

Puhr, H. 2021. globaltrends (v.0.0.8): Measure global trends with Google Trends, R Package.

Available at https://github.com/ha-pu/globaltrends.

28

Rashid, B. 2017. How these two 17-year-olds are cashing in on the fidget spinners everyone is talking

about, Forbes Magazine. New York.

Rugman, A. M. 1980. A new theory of multinational enterprise - Internationalization versus

internalization. Columbia Journal of World Business, 15(1): 23-29.

Rugman, A. M. & Verbeke, A. 2004. A perspective on regional and global strategies of multinational

enterprises. Journal of International Business Studies, 35(1): 3-18.

Sherman-Morris, K., Senkbeil, J., & Carver, R. 2011. Who's Googling what? What internet searches

reveal about hurricane information seeking. Bulletin of the American Meteorological Society,

92(8): 975-85.

Sullivan, D. 1994. Measuring the degree of internationalization of a firm. Journal of International

Business Studies, 25(2): 325-42.

Verbeke, A. & Forootan, M. Z. 2012. How good are multinationality–performance (M-P) empirical

studies? Global Strategy Journal, 2(4): 332-44.

Verbeke, A., Li, L., & Goerzen, A. 2009. Toward more effective research on the multinationality-

performance relationship. Management International Review, 49(2): 149-61.

Vlastakis, N. & Markellos, R. N. 2012. Information demand and stock market volatility. Journal of

Banking & Finance, 36(6): 1808-21.

Vosen, S. & Schmidt, T. 2011. Forecasting private consumption: Survey‐based indicators vs. Google

Trends. Journal of forecasting, 30(6): 565-78.

Welch, C., Nummela, N., & Liesch, P. 2016. The internationalization process model revisited: An

agenda for future research. Management International Review, 56(6): 783-804.

Yang, X., Pan, B., Evans, J. A., & Lv, B. F. 2015. Forecasting Chinese tourist volume with search

engine data. Tourism Management, 46: 386-97.

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Table 1: Descriptive statistics for VOI and DOI measures for S&P 500

Parameter N Mean SD Min Max

VOIobserved 1,025 0.003 0.024 0.000 0.666

DOI Giniobserved 1,025 0.166 0.158 0.000 0.721

DOI Giniseason-adjusted 1,025 0.192 0.163 0.000 0.713

DOI Ginitrend-only 1,025 0.215 0.179 0.000 0.739

DOI HHIobserved 1,025 0.675 0.328 0.000 0.980

DOI Entropyobserved 1,025 -0.414 0.510 -3.157 0.000

Share of foreign assetsS&P500 527 0.158 0.206 0.000 1.000

Share of foreign salesS&P500 527 0.226 0.228 0.000 1.000

Share of foreign profitsS&P500 527 0.054 0.177 -0.669 1.000

RIMSMarshall et al. (2020) 484 0.139 0.158 0.000 0.766

TNCIUNCTAD (2020) 93 59.609 21.462 4.314 99.590

Number of foreign countries 1,025 23.710 22.378 1.000 151.000

Share of foreign subsidiaries 994 0.353 0.259 0.000 1.000

Note: Subscript indicates comparison dataset. S&P 500 data were obtained from annual reports filed

with the SEC. TNC Index was provided by UNCTAD and RIMS by Marshall, Brouthers, and Keig

(2020). Number of foreign countries and share of foreign subsidiaries was obtained from BvD Orbis

for the full sample.

30

Table 2: Correlation table for VOI and DOI from Google Trends and traditional DOI measures

Parameter 13 12 11 10 9 8 7 6 5 4 3 2 1

1 VOIobserved -0.012 0.043 -0.174 0.098 -0.020 0.054 0.020 -0.028 0.093 0.206 0.231 0.254 1

2 DOI Giniobserved 0.180 0.430 0.024 0.364 0.134 0.378 0.250 -0.212 0.702 0.975 0.990 1

3 DOI Giniseason-adjusted 0.183 0.435 0.006 0.369 0.135 0.389 0.251 -0.213 0.743 0.995 1

4 DOI Ginitrend-only 0.186 0.436 0.009 0.372 0.127 0.394 0.255 -0.199 0.761 1

5 DOI HHIobserved 0.119 0.355 0.027 0.367 0.094 0.374 0.271 -0.262 1

6 DOI Entropyobserved -0.021 -0.127 0.025 -0.122 -0.043 0.071 0.038 1

7 Share of foreign assetsS&P500 0.351 0.287 0.627 0.161 0.182 0.497 1

8 Share of foreign salesS&P500 0.450 0.383 0.771 0.293 0.127 1

9 Share of foreign profitsS&P500 0.041 0.091 0.161 0.070 1

1

0 RIMSMarshall et al. (2020) 0.087 0.407 -0.132 1

1

1 TNCIUNCTAD (2020) 0.345 0.390 1

1

2 Number of foreign countries 0.529 1

1

3 Share of foreign subsidiaries 1

Note: Subscript indicates comparison dataset. S&P 500 data were obtained from annual reports filed with the SEC. TNC Index was provided by UNCTAD and

RIMS by Marshall, Brouthers, and Keig (2020). Number of foreign countries and share of foreign subsidiaries was obtained from BvD Orbis for the full sample.

31

Table 3: Empirical comparison of Google Trends DOI and traditional DOI measures

Google Trends DOI (1) (2) (3)

Traditional DOI 0.259

p = 0.000

Number of foreign countries 0.338

p = 0.000

Share of foreign subsidiaries 0.135 p = 0.000

Constant 0.269 -0.001 -0.002

p = 0.000 p = 0.964 p = 0.940

Observations 1,103 1,025 1,025

Dataset fixed effects Included

R2 0.150 0.114 0.018

Adjusted R2 0.148 0.113 0.017

Note: In the predictive model, we pool all available firm-level datasets (i.e., S&P 500, RIMS, TNCI) and

include an indicator variable to capture dataset-related differences. Traditional DOI measures are

standardized to establish comparability. In Model 1, we use traditional measures of DOI from existing

datasets as a predictor variable for the Google Trends DOI. Results indicate significant positive predictive

relationship with reasonable reliability (R2). In Models 2 and 3, we use measures for DOI we can obtain

for all companies as predictors (i.e., number of foreign subsidiaries, share of foreign subsidiaries). Results

confirm Model 1.

32

Table 4: Empirical tests for country biases of Google Trends DOI measures

Google Trends DOI (4) (5) (6) (7) (8)

Traditional DOI 0.371 0.224 0.223 0.370 0.427

p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000

GDP 0.049

p = 0.008

GDP/capita 0.332

p = 0.001

Internet usage 0.077

p = 0.605

English proficiency 0.054

p = 0.190

English as official language 0.187 p = 0.019

Traditional DOI x

GDP

-0.034

p = 0.034

Traditional DOI x

GDP/capita

0.053

p = 0.521

Traditional DOI x

Internet usage

0.092

p = 0.506

Traditional DOI x

English proficiency

-0.185

p = 0.000

Traditional DOI x

English as official language

-0.236 p = 0.000

Constant 0.057 0.110 0.238 0.217 0.086

p = 0.521 p = 0.077 p = 0.002 p = 0.000 p = 0.326

Observations 1,096 1,096 1,098 1,089 1,099


R2 0.161 0.161 0.152 0.171 0.164

Adjusted R2 0.157 0.157 0.148 0.167 0.161

Note: In Models 4 to 8, we test for home country-related biases that have been mentioned in connection

with Google Trends by interacting traditional DOI as a predictor with a bias-related construct. A

significant interaction indicates a significant bias that influences the predictive capacity of traditional DOI

on Google Trends DOI. In Models 4 and 5, we test for home country size effects indicating a small but

significant bias. In Models 6 we test for biases from varying degrees of internet usage in countries. In

models 7 and 8, we test for language related biases using English proficiency scores and a dummy for

English as a native language. Results indicate significant but small biases from languages. Across all

specification, the predictive validity of traditional DOI on Google Trends DOI remains positive and

significant.

33

Table 5: Empirical tests for firm biases of Google Trends DOI measures

Google Trends DOI (9) (10) (11) (12) (13)

Traditional DOI 0.273 0.265 0.280 0.343 0.258

p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000

Total sales 0.322

p = 0.000

Number of employees 0.268 p = 0.000

Total assets 0.248 p = 0.000

B2B industry -0.019

p = 0.825

Exposure to China -0.097

p = 0.043

Traditional DOI x

Total sales

0.073

p = 0.003

Traditional DOI x

Number of employees

0.104 p = 0.001

Traditional DOI x

Total assets

0.174 p = 0.001

Traditional DOI x

B2B industry

-0.097

p = 0.244

Traditional DOI x

Exposure to China

-0.022

p = 0.547

Constant 0.265 0.253 0.268 0.300 0.244

p = 0.000 p = 0.000 p = 0.000 p = 0.000 p = 0.000

Observations 1,077 1,074 1,075 1,093 1,084


R2 0.238 0.205 0.174 0.152 0.153

Adjusted R2 0.234 0.202 0.170 0.148 0.149

Note: In Models 9 to 13, we test for firm-specific biases by interacting traditional DOI as a predictor with

a bias-related construct. A significant interaction indicates a significant bias that influences the predictive

capacity of traditional DOI on Google Trends DOI. In Models 9 to 11, we test for firm size biases

indicating a small but significant bias. In Model 12 we test for biases for B2C firms. In Model 13, we test

biases affecting firms with operations in China using the number of Chinese subsidiaries as a moderator.

Both B2B and China biases are insignificant.

34

Table 6: Areas of application for accounting-based and Google Trends measures of

internationalization

Accounting-based measures Google Trends measures

Primary theoretical mechanisms

Operational flexibility

(e.g., real options, internal capital markets)

Market-based mechanisms

(e.g., stock-price reactions)

Operational complexity

(e.g., costs of coordination)

Signaling-based mechanisms

(e.g., brand, reputation, popularity)

Organizational structure

(e.g., subsidiary network)

Stakeholder diversity

(e.g., stakeholder network)

International diversification

(e.g., political risk diversification)

Global exposure

(e.g., visibility, recognition, political risk)

Temporal orientation

Static internationalization status

(e.g., operational footprint)

Internationalization dynamics

(e.g., rapid internationalization,

de-internationalization)

Note: The table contrasts the suitability of traditional and Google Trends measures of firm

internationalization according to the primary theoretical mechanisms and temporal orientation of the

effect studied.

35

Figure 1: VOI and DOI as two distinct measures of internationalization

Note: In this numeric example we do not include the domestic market for simplicity.

36

Figure 2: VOI and DOI of the Fidget Spinner

Note: The first panel depicts the fidget spinner’s rise to becoming a global product in just a matter of

weeks using the VOI measure. The second panel illustrates the DOI over the same period for the fidget

spinner. It highlights that despite its quick decline in volume, the fidget spinner remained a globally

internationalized product with similarly distributed interests across markets.

37

Figure 3: Exemplary distributions of DOI

Note: The figure illustrates DOI boxplots for six selected S&P 500 companies. Three companies (Coca-

Cola, Facebook & Microsoft) were selected because of their high DOI and three companies (Alaska Air,

Illinois Tool Works & JM Smucker) were selected because of their low DOI. Comparison of average DOI

between companies and the tight distribution boxplots within companies over time indicate high validity

and reliability of Google Trends DOI measures.

38

Figure 4: Exemplary time series of DOI

Note: The figure illustrates DOI time series for six selected S&P 500 companies. Three companies (Coca-

Cola, Facebook & Microsoft) were selected because of their high DOI and three companies (Alaska Air,

Illinois Tool Works & JM Smucker) were selected because of their low DOI. Relatively stable DOI

measures across time indicate high validity and reliability of Google Trends DOI measures.

39

Figure 5: Scatter plot of average VOI and DOI for S&P 500

Note: The figure plots monthly VOI measures against DOI measures for firms in the S&P 500. As

expected, companies with low volume in search activity tend to be less internationalized (DOI). Among

companies of large volume (VOI), however, there exists extensive variation between VOI and DOI. Some

companies are described as more local giants while others are multinational giants.

40

Figure 6: Marginal effect plots for bias regressions

Note: The figure shows marginal effect plots for the bias regressions presented in Tables 3-5. We use

traditional DOI measures (FSTS, RIMS, TNCI) to predict Google Trends DOI. To analyze the robustness

of the Google Trends DOI measure, we introduce a series of biases.

41

Appendix

Figure A1: Global Market-share of Google

Note: Obtained Jan 21st 2021.

42

Figure A2: Google Trends-related publications (Web of Science + PubMed)

Note: Data combines PubMed and Web of Science articles that include “Google Trends” as topic,

keyword or in the title. Data as of 21.09.2020.

0

50

100

150

200

250

300

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Nr. of scientific publications

43

Figure A3: Web of science & Pub Med word bigram


keyword or in the title. Data as of 21.09.2020. Word clouds were computed from author supplied

keywords in PubMed and Web of Science.

44

Figure A4: Across and within country search scores of the Fidget Spinner

Note: The figure illustrates the rise of the fidget spinner within the US (lower panel) and slightly later

across the world (upper panel). Search score is measured using VOI.

45

Figure A5: Seasonality effects in VOI and DOI

Note: The figure shows VOI and DOI time series for Amazon. Spikes indicate surges in interest (VOI)

around black Friday and Christmas (dotted lines). Because these events are concentrated in the US and

Western Europe, the degree of internationalization tends to reflect these concentrated surges with lower

DOI.

46

Figure A6: Observed, season-adjusted, and trend-only search scores of Coca-Cola

Note: The figure plots observed search scores against season-adjusted search scores (left panel) and trend-

only search scores (right panel). The strong correlation between observed search scores and adjusted

search scores indicates robustness to distortions from seasonality and outlier effects.

47

Figure A7: DOI of Coca-Cola based on Gini coefficient, HHI, and Entropy

Note: The figure plots DOI measures as inverted Gini coefficient against DOI as inverted Herfindahl

index (left panel) and as inverted Entropy (right panel). The strong correlation between Gini-based DOI

and the alternative DOI measurements indicate robustness to distortions from measurement.

48

Figure A8: Additional marginal effect plots for bias regressions

Note: The figure shows additional marginal effect plots for the bias regressions presented in Tables 3-5

(c.f. Figure 6). We use traditional DOI measures (FSTS, RIMS, TNCI) to predict Google Trends DOI. To

analyze the robustness of the Google Trends DOI measure, we introduce a series of biases.

49

Figure A9: Google Market Share in China

Note: Obtained Jan 21st 2021.

50

Figure A10: Google Trends results for Apple and Microsoft in China and Russia

China

Apple Microsoft

Russia

Apple Microsoft

Note: The Figure shows search volumes for “Apple” and “Microsoft” in China and Russia from Google

Trends. Obtained Feb 15th 2021.

51

Figure A11: DOI for products

Note: The figure shows DOI for different products such as cars and electronic gadgets. In the case

of the Tesla Model 3 and the Nintendo Switch, the rapid increase in internationalization after the product

launch in apparent. For the Amazon Kindle and the VW Golf, internationalization remains relatively

stable.

52

Figure A12: DOI for individuals

Note: The figure shows DOI for various individuals. In the case of athlete Kylian Mbappe, his rise in

fame and recognition after the 2018 football world cup is apparent in the data.

53

Figure A13: DOI for non-corporate organizations

Note: The figure shows DOI for various non-corporate organizations.

54

Figure A14: DOI for phenomena and trends

Note: The figure shows DOI for various social phenomena and trends. In the case of Covid-19 and the Ice Bucket Challenge, the data shows the rapid increase in international interest.

55

Figure A15: Event studies with VOI and DOI

Note: The figure shows abnormal monthly changes in VOI and DOI for selected events (solid red line) compared to a 12-month historic average. The two upper panels indicate developments around the acquisition announcement of Tiffany by LMVH. Higher VOI but lower DOI hints at greater but

more concentrated interest in Tiffany. Probably by geographically concentrated investors. The third panel

shows the increase in global interest (higher DOI) in Esther Duflo after she was announced as a Nobel

laureate. The lower panel shows the increase in global interest (higher DOI) in the Juventus Turin football

club after the announced transfer of Cristiano Ronaldo to Turin.

56

Figure A16: Example for intra-country dispersion for US newspapers

Note: The figure illustrates geographical distributions of newspapers within the US and shows search

scores for individual US states. While the Boston Globe and Star tribune are concentrated strongly in

Massachusetts and Minnesota, the Wall Street Journal is more dispersed but dominant in Washington DC.

USA Today, is most evenly distributed across all US states and has highest degree of intra-country

dispersion.

57

Figure A17: Socio-political country risk score computed from Google Trends data

Note: The figure shows preliminary results for a socio-political risk index for various countries. The

authors computed the risk index based on search scores for 106 risk-related search terms obtained from

globaltrends. The more internet users search for terms such as “conflict”, “corruption”, or “terror”, the

more pervasive the socio-political issue in the respective country.

58

Table A1: Overview of disciplines using Google Trends

Discipline Nr. Papers

Health 545

Economics 108

Social Science 63

Business 41

Tourism 32

Political Science 20

Education 12

Psychology 8

Ecology 7

Computer Science 5

Sports 5

Meteorology 3

Biology 3

Geography 3

Grand Total 855


keyword or in the title. Data as of 21.09.2020.

59

Table A2: Academic article overview 2000-2020 in Web of Science & Pub Med

Title Authors Journal/Source Year Vol. Iss. Citation

s

Cit./Year

Predicting the Present with Google Trends Choi, Hyunyoung; Varian, Hal ECONOMIC RECORD 2012 88

629 69.89

Quantifying Trading Behavior in Financial Markets

Using Google Trends

Preis, Tobias; Moat, Helen Susannah;

Stanley, H. Eugene

SCIENTIFIC

REPORTS

2013 3

280 35

The Use of Google Trends in Health Care

Research: A Systematic Review

Nuti, Sudhakar V.; Wayda, Brian;

Ranasinghe, Isuru; Wang, Sisi; Dreyer,

Rachel P.; Chen, Serene I.; Murugiah,

Karthik

PLOS ONE 2014 9 10 225 32.14

BitCoin meets Google Trends and Wikipedia:

Quantifying the relationship between phenomena of

the Internet era

Kristoufek, Ladislav SCIENTIFIC

REPORTS

2013 3

192 24

Forecasting Private Consumption: Survey-Based

Indicators vs. Google Trends

Vosen, Simeon; Schmidt, Torsten JOURNAL OF

FORECASTING

2011 30 6 174 17.4

Information demand and stock market volatility Vlastakis, Nikolaos; Markellos, Raphael N. JOURNAL OF

BANKING &

FINANCE

2012 36 6 153 17

Forecasting Chinese tourist volume with search

engine data

Yang, Xin; Pan, Bing; Evans, James A.; Lv,

Benfu

TOURISM

MANAGEMENT

2015 46

134 22.33

Can Google data improve the forecasting

performance of tourist arrivals? Mixed-data

sampling approach

Bangwayo-Skeete, Prosper F.; Skeete, Ryan

W.

TOURISM

MANAGEMENT

2015 46

109 18.17

The utility of Google Trends for epidemiological

research: Lyme disease as an example

Seifter, Ari; Schwarzwalder, Alison; Geis,

Kate; Aucott, John

GEOSPATIAL

HEALTH

2010 4 2 109 9.91

Big data in tourism research: A literature review Li, Jingjing; Xu, Lizhi; Tang, Ling; Wang,

Shouyang; Li, Ling

TOURISM

MANAGEMENT

2018 68

86 28.67

Google search patterns suggest declining interest in

the environment

Mccallum, Malcolm L.; Bury, Gwendolyn

W.

BIODIVERSITY AND

CONSERVATION

2013 22 6-7 76 9.5

Characteristics of Bitcoin users: an analysis of

Google search data

Yelowitz, Aaron; Wilson, Matthew APPLIED

ECONOMICS

LETTERS

2015 22 13 73 12.17

Nowcasting with Google Trends in an Emerging

Market

Carriere-Swallow, Yan; Labbe, Felipe JOURNAL OF

FORECASTING

2013 32 4 70 8.75

Media Influences on Social Outcomes: The Impact

of MTV's 16 and Pregnant on Teen Childbearing

Kearney, Melissa S.; Levine, Phillip B. AMERICAN

ECONOMIC REVIEW

2015 105 12 59 9.83

Measuring economic uncertainty and its impact on

the stock market

Dzielinski, Michal FINANCE RESEARCH

LETTERS

2012 9 3 55 6.11

Google searches and stock returns Bijl, Laurens; Kringhaug, Glenn; Molnar,

Peter; Sandvik, Eirik

INTERNATIONAL

REVIEW OF

FINANCIAL

ANALYSIS

2016 45

54 10.8

60


s

Cit./Year

Can Google Trends search queries contribute to

risk diversification?

Kristoufek, Ladislav SCIENTIFIC

REPORTS

2013 3

49 6.13

Is Google Trends a reliable tool for digital

epidemiology? Insights from different clinical

settings

Cervellin, Gianfranco; Comelli, Ivan; Lippi,

Giuseppe

JOURNAL OF

EPIDEMIOLOGY

AND GLOBAL

HEALTH

2017 7 3 48 12

Forecasting Destination Weekly Hotel Occupancy

with Big Data

Pan, Bing; Yang, Yang JOURNAL OF

TRAVEL RESEARCH

2017 56 7 48 12

Oil financialization and volatility forecast:

Evidence from multidimensional predictors

Ma, Yan-ran; Ji, Qiang; Pan, Jiaofeng JOURNAL OF

FORECASTING

2019 38 6 43 21.5

Analysis of the Capacity of Google Trends to

Measure Interest in Conservation Topics and the

Role of Online News

Nghiem, Le T. P.; Papworth, Sarah K.;

Lim, Felix K. S.; Carrasco, Luis R.

PLOS ONE 2016 11 3 42 8.4

Searching for the picture: forecasting UK cinema

admissions using Google Trends data

Hand, Chris; Judge, Guy APPLIED

ECONOMICS

LETTERS

2012 19 11 42 4.67

A dynamic linear model to forecast hotel

registrations in Puerto Rico using Google Trends

data

Rivera, Roberto TOURISM

MANAGEMENT

2016 57

40 8

Do American States with More Religious or

Conservative Populations Search More for Sexual

Content on Google?

MacInnis, Cara C.; Hodson, Gordon ARCHIVES OF

SEXUAL BEHAVIOR

2015 44 1 40 6.67

Skip the Trip: Air Travelers' Behavioral Responses

to Pandemic Influenza

Fenichel, Eli P.; Kuminoff, Nicolai V.;

Chowell, Gerardo

PLOS ONE 2013 8 3 37 4.63

Novel surveillance of psychological distress during

the great recession

Ayers, John W.; Althouse, Benjamin M.;

Allem, Jon-Patrick; Childers, Matthew A.;

Zafar, Waleed; Latkin, Carl; Ribisl, Kurt

M.; Brownstein, John S.

JOURNAL OF

AFFECTIVE

DISORDERS

2012 142 1-3 37 4.11

Exploring new web-based tools to identify public

interest in science

Baram-Tsabari, Ayelet; Segev, Elad PUBLIC

UNDERSTANDING

OF SCIENCE

2011 20 1 37 3.7

Short-term forecasting of Japanese tourist inflow to

South Korea using Google trends data

Park, Sangkon; Lee, Jungmin; Song,

Wonho

JOURNAL OF

TRAVEL & TOURISM

MARKETING

2017 34 3 36 9

Daily happiness and stock returns: Some

international evidence

Zhang, Wei; Li, Xiao; Shen, Dehua; Teglio,

Andrea

PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2016 460

35 7

Does twitter predict Bitcoin? Shen, Dehua; Urquhart, Andrew; Wang,

Pengfei

ECONOMICS

LETTERS

2019 174

34 17

Can internet searches forecast tourism inflows? Artola, Concha; Pinto, Fernando; de

Pedraza Garcia, Pablo

INTERNATIONAL

JOURNAL OF

MANPOWER

2015 36 1 33 5.5

61


s

Cit./Year

Data Mining From Web Search Queries: A

Comparison of Google Trends and Baidu Index

Vaughan, Liwen; Chen, Yue JOURNAL OF THE

ASSOCIATION FOR

INFORMATION

SCIENCE AND

TECHNOLOGY

2015 66 1 33 5.5

Retail investor attention and stock liquidity Ding, Rong; Hou, Wenxuan JOURNAL OF

INTERNATIONAL

FINANCIAL

MARKETS

INSTITUTIONS &

MONEY

2015 37

32 5.33

Everything you always wanted to know about log-

periodic power laws for bubble modeling but were

afraid to ask

Geraskin, Petr; Fantazzini, Dean EUROPEAN

JOURNAL OF

FINANCE

2013 19 5 32 4

A Structured Analysis of Unstructured Big Data by

Leveraging Cloud Computing

Liu, Xiao; Singh, Param Vir; Srinivasan,

Kannan

MARKETING

SCIENCE

2016 35 3 31 6.2

Decomposing the Impact of Advertising:

Augmenting Sales with Online Search Data

Hu, Ye; Du, Rex Yuxing; Damangir, Sina JOURNAL OF

MARKETING

RESEARCH

2014 51 3 31 4.43

Social media analytics: Extracting and visualizing

Hilton hotel ratings and reviews from TripAdvisor

Chang, Yung-Chun; Ku, Chih-Hao; Chen,

Chun-Hung

INTERNATIONAL

JOURNAL OF

INFORMATION

MANAGEMENT

2019 48

30 15

Tourism demand forecasting: A deep learning

approach

Law, Rob; Li, Gang; Fong, Davis Ka Chio;

Han, Xin

ANNALS OF

TOURISM

RESEARCH

2019 75

30 15

Leveraging Trends in Online Searches for Product

Features in Market Response Modeling

Du, Rex Yuxing; Hu, Ye; Damangir, Sina JOURNAL OF

MARKETING

2015 79 1 29 4.83

Where and When Can We Use Google Trends to

Measure Issue Salience?

Mellon, Jonathan PS-POLITICAL

SCIENCE &

POLITICS

2013 46 2 29 3.63

Ten years of research change using Google Trends:

From the perspective of big data utilizations and

applications

Jun, Seung-Pyo; Yoo, Hyoung Sun; Choi,

San

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2018 130

28 9.33

Investor sentiment and the price of oil Qadan, Mahmoud; Nama, Hazar ENERGY

ECONOMICS

2018 69

28 9.33

Forecasting volatility with empirical similarity and

Google Trends

Hamid, Alain; Heiden, Moritz JOURNAL OF

ECONOMIC

BEHAVIOR &

ORGANIZATION

2015 117

28 4.67

Forecasting unemployment with internet search

data: Does it help to improve predictions when job

destruction is skyrocketing?

Rosalia Vicente, Maria; Lopez-Menendez,

Ana J.; Perez, Rigoberto

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2015 92

28 4.67

62


s

Cit./Year

Assessing the Methods, Tools, and Statistical

Approaches in Google Trends Research:

Systematic Review

Mavragani, Amaryllis; Ochoa, Gabriela;

Tsagarakis, Konstantinos P.

JOURNAL OF

MEDICAL INTERNET

RESEARCH

2018 20 11 27 9

Technology-push and demand-pull factors in

emerging sectors: evidence from the electric

vehicle market

Choi, Hyundo INDUSTRY AND

INNOVATION

2018 25 7 27 9

Has microblogging changed stock market

behavior? Evidence from China

Jin, Xi; Shen, Dehua; Zhang, Wei PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2016 452

27 5.4

Google Trends and tourists' arrivals: Emerging

biases and proposed corrections

Dergiades, Theologos; Mavragani, Eleni;

Pan, Bing

TOURISM

MANAGEMENT

2018 66

26 8.67

Limited Edition for Me and Best Seller for You:

The Impact of Scarcity versus Popularity Cues on

Self versus Other-Purchase Behavior

Wu, Laurie; Lee, Christopher JOURNAL OF

RETAILING

2016 92 4 26 5.2

Forecasting German car sales using Google data

and multivariate models

Fantazzini, Dean; Toktamysova, Zhamal INTERNATIONAL

JOURNAL OF

PRODUCTION

ECONOMICS

2015 170

26 4.33

Power-law correlations in finance-related Google

searches, and their cross-correlations with volatility

and traded volume: Evidence from the Dow Jones

Industrial components

Kristoufek, Ladislav PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2015 428

26 4.33

Online big data-driven oil consumption forecasting

with Google trends

Yu, Lean; Zhao, Yaqing; Tang, Ling; Yang,

Zebin

INTERNATIONAL

JOURNAL OF

FORECASTING

2019 35 1 24 12

Tweets, Google trends, and sovereign spreads in

the GIIPS

Dergiades, Theologos; Milas, Costas;

Panagiotidis, Theodore

OXFORD ECONOMIC

PAPERS-NEW

SERIES

2015 67 2 24 4

The internet as a data source for advancement in

social sciences

Askitas, Nikolaos; Zimmermann, Klaus F. INTERNATIONAL

JOURNAL OF

MANPOWER

2015 36 1 24 4

Web traffic and organization performance

measures: Relationships and data sources examined

Vaughan, Liwen; Yang, Rongbin JOURNAL OF

INFORMETRICS

2013 7 3 24 3

The causal relationship between Bitcoin attention

and Bitcoin returns: Evidence from the Copula-

based Granger causality test

Dastgir, Shabbir; Demir, Ender; Downing,

Gareth; Gozgor, Giray; Lau, Chi Keung

Marco

FINANCE RESEARCH

LETTERS

2019 28

23 11.5

Bariatric surgery interest around the world: What

Google Trends can teach us

Linkov, Faina; Bovbjerg, Dana H.; Freese,

Kyle E.; Ramanathan, Ramesh; Eid, George

Michel; Gourash, William

SURGERY FOR

OBESITY AND

RELATED DISEASES

2014 10 3 22 3.14

Predicting short-term stock prices using ensemble

methods and online data sources

Weng, Bin; Lu, Lin; Wang, Xing;

Megahed, Fadel M.; Martinez, Waldyn

EXPERT SYSTEMS

WITH

APPLICATIONS

2018 112

21 7

63


s

Cit./Year

A monthly consumption indicator for Germany

based on Internet search query data

Vosen, Simeon; Schmidt, Torsten APPLIED

ECONOMICS

LETTERS

2012 19 7 21 2.33

Google It Up! A Google Trends Uncertainty index

for the United States and Australia

Castelnuovo, Efrem; Trung Duc Tran ECONOMICS

LETTERS

2017 161

20 5

Advertising and Word-of-Mouth Effects on Pre-

launch Consumer Interest and Initial Sales of

Experience Products

Kim, Ho; Hanssens, Dominique M. JOURNAL OF

INTERACTIVE

MARKETING

2017 37

20 5

Monitoring of non-cigarette tobacco use using

Google Trends

Cavazos-Rehg, Patricia A.; Krauss, Melissa

J.; Spitznagel, Edward L.; Lowery, Ashley;

Grucza, Richard A.; Chaloupka, Frank J.;

Bierut, Laura Jean

TOBACCO CONTROL 2015 24 3 20 3.33

An empirical study of users' hype cycle based on

search traffic: the case study on hybrid cars

Jun, Seung-Pyo SCIENTOMETRICS 2012 91 1 20 2.22

A rapid method for assessing social versus

independent interest in health issues: A case study

of 'bird flu' and 'swine flu'

Bentley, R. Alexander; Ormerod, Paul SOCIAL SCIENCE &

MEDICINE

2010 71 3 20 1.82

Predicting the direction of stock markets using

optimized neural networks with Google Trends

Hu, Hongping; Tang, Li; Zhang, Shuhua;

Wang, Haiyan

NEUROCOMPUTING 2018 285

19 6.33

Forecasting tourism demand with Google trends:

Accuracy comparison of countries versus cities

Oender, Irem INTERNATIONAL

JOURNAL OF

TOURISM

RESEARCH

2017 19 6 19 4.75

Has the American Public's Interest in Information

Related to Relationships Beyond The Couple

Increased Over Time?

Moors, Amy C. JOURNAL OF SEX

RESEARCH

2017 54 6 19 4.75

Perceived pollution and inbound tourism in China Xu, Xu; Reed, Markum TOURISM

MANAGEMENT

PERSPECTIVES

2017 21

19 4.75

Celebrity suicide on the railway network: Can one

case trigger international effects?

Koburger, Nicole; Mergl, Roland; Rummel-

Kluge, Christine; Ibelshaeuser, Angela;

Meise, Ullrich; Postuvan, Vita; Roskar,

Saska; Szekely, Andras; Toth, Monika

Ditta; van der Feltz-Cornelis, Christina;

Hegerl, Ulrich

JOURNAL OF

AFFECTIVE

DISORDERS

2015 185

19 3.17

Web Search Volume as a Predictor of Academic

Fame: An Exploration of Google Trends

Vaughan, Liwen; Romero-Frias, Esteban JOURNAL OF THE

ASSOCIATION FOR

INFORMATION

SCIENCE AND

TECHNOLOGY

2014 65 4 19 2.71

Public interest in the environment is falling: a

response to Ficetola (2013)

McCallum, Malcolm L.; Bury, Gwendolynn

W.

BIODIVERSITY AND

CONSERVATION

2014 23 4 19 2.71

64


s

Cit./Year

WHO'S GOOGLING WHAT? What Internet

Searches Reveal about Hurricane Information

Seeking

Sherman-Morris, Kathleen; Senkbeil,

Jason; Carver, Robert

BULLETIN OF THE

AMERICAN

METEOROLOGICAL

SOCIETY

2011 92 8 19 1.9

The Collaborative Economy Based Analysis of

Demand: Study of Airbnb Case in Spain and

Portugal

Palos-Sanchez, Pedro R.; Correia, Marisol

B.

JOURNAL OF

THEORETICAL AND

APPLIED

ELECTRONIC

COMMERCE

RESEARCH

2018 13 3 18 6

Sleep and moral awareness Barnes, Christopher M.; Gunia, Brian C.;

Wagner, David T.

JOURNAL OF SLEEP

RESEARCH

2015 24 2 18 3

Public hospital quality report awareness: evidence

from National and Californian Internet searches and

social media mentions, 2012

Huesch, Marco D.; Currid-Halkett,

Elizabeth; Doctor, Jason N.

BMJ OPEN 2014 4 3 18 2.57

Using Search Engine Query Data to Track

Pharmaceutical Utilization: A Study of Statins

Schuster, Nathaniel M.; Rogers, Mary A.

M.; McMahon, Laurence F., Jr.

AMERICAN

JOURNAL OF

MANAGED CARE

2010 16 8 18 1.64

Google Trends and reality: Do the proportions

match? Appraising the informational value of

online search behavior: Evidence from Swiss

tourism regions

Siliverstovs, Boriss; Wochner, Daniel S. JOURNAL OF

ECONOMIC

BEHAVIOR &

ORGANIZATION

2018 145

17 5.67

Research note: Nowcasting tourist arrivals in

Barbados - just Google it!

Jackman, Mahalia; Naitram, Simon TOURISM

ECONOMICS

2015 21 6 17 2.83

YES or NO: Predicting the 2015 GReferendum

results using Google Trends

Mavragani, Amaryllis; Tsagarakis,

Konstantinos P.

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2016 109

16 3.2

Google Trends (R): Ready for real-time suicide

prevention or just a Zeta-Jones effect? An

exploratory study

Fond, Guillaume; Gaman, Alexandru;

Brunel, Lore; Haffen, Emmanuel; Llorca,

Pierre-Michel

PSYCHIATRY

RESEARCH

2015 228 3 16 2.67

Seasonal trends in sleep-disordered breathing:

evidence from Internet search engine query data

Ingram, David G.; Matthews, Camilla K.;

Plante, David T.

SLEEP AND

BREATHING

2015 19 1 16 2.67

Characterizing the Time-Perspective of Nations

with Search Engine Query Data

Noguchi, Takao; Stewart, Neil; Olivola,

Christopher Y.; Moat, Helen Susannah;

Preis, Tobias

PLOS ONE 2014 9 4 16 2.29

Quantifying the cross-correlations between online

searches and Bitcoin market

Zhang, Wei; Wang, Pengfei; Li, Xiao;

Shen, Dehua

PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2018 509

15 5

INTENTIONAL MISUSE AND ABUSE OF

LOPERAMIDE: A NEW LOOK AT A DRUG

WITH LOW ABUSE POTENTIAL''

Borron, Stephen W.; Watts, Susan H.; Tull,

Jonathan; Baeza, Salvador; Diebold,

Stephanie; Barrow, Alison

JOURNAL OF

EMERGENCY

MEDICINE

2017 53 1 15 3.75

65


s

Cit./Year

Forecasting state-level premature deaths from

alcohol, drugs, and suicides using Google Trends

data

Parker, Jason; Cuthbertson, Courtney;

Loveridge, Scott; Skidmore, Mark; Dyar,

Will

JOURNAL OF

AFFECTIVE

DISORDERS

2017 213

15 3.75

A Google-based approach for monitoring suicide

risk

Solano, Paola; Ustulin, Morena; Pizzorno,

Enrico; Vichi, Monica; Pompili, Maurizio;

Serafini, Gianluca; Amore, Mario

PSYCHIATRY

RESEARCH

2016 246

15 3

Estimating suicide occurrence statistics using

Google Trends

Kristoufek, Ladislav; Moat, Helen

Susannah; Preis, Tobias

EPJ DATA SCIENCE 2016 5

15 3

A study of the method using search traffic to

analyze new technology adoption

Jun, Seung-Pyo; Yeom, Jaeho; Son, Jong-

Ku

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2014 81

15 2.14

The importance of international conferences on

sustainable development as higher education

institutions' strategies to promote sustainability: A

case study in Brazil

Berchin, Issa Ibrahim; Sima, Mihaela; de

Lima, Mauricio Andrade; Biesel, Shelly;

dos Santos, Leandro Piazza; Ferreira,

Renata Vilma; Osorio de Andrade Guerra,

Jose Baltazar Salgueirinho; Ceci, Flavio

JOURNAL OF

CLEANER

PRODUCTION

2018 171

14 4.67

Quantifying potential tourist behavior in choice of

destination using Google Trends

Padhi, Sidhartha S.; Pati, Rupesh K. TOURISM

MANAGEMENT

PERSPECTIVES

2017 24

14 3.5

Low validity of Google Trends for behavioral

forecasting of national suicide rates

Tran, Ulrich S.; Andel, Rita;

Niederkrotenthaler, Thomas; Till, Benedikt;

Ajdacic-Gross, Vladeta; Voracek, Martin

PLOS ONE 2017 12 8 14 3.5

Google Trends can improve surveillance of Type 2

diabetes

Tkachenko, Nataliya; Chotvijit, Sarunkorn;

Gupta, Neha; Bradley, Emma; Gilks,

Charlotte; Guo, Weisi; Crosby, Henry;

Shore, Eliot; Thiarai, Malkiat; Procter, Rob;

Jarvis, Stephen

SCIENTIFIC

REPORTS

2017 7

14 3.5

Characterizing Awareness of Schizophrenia Among

Facebook Users by Leveraging Facebook

Advertisement Estimates

Saha, Koustuv; Weber, Ingmar; Birnbaum,

Michael L.; De Choudhury, Munmun

JOURNAL OF

MEDICAL INTERNET

RESEARCH

2017 19 5 14 3.5

Could Google Trends Be Used to Predict

Methamphetamine-Related Crime? An Analysis of

Search Volume Data in Switzerland, Germany, and

Austria

Gamma, Alex; Schleifer, Roman;

Weinmann, Wolfgang; Buadze, Anna;

Liebrenz, Michael

PLOS ONE 2016 11 11 14 2.8

Forecasting tourist arrivals at attractions: Search

engine empowered methodologies

Volchek, Katerina; Liu, Anyu; Song,

Haiyan; Buhalis, Dimitrios

TOURISM

ECONOMICS

2019 25 3 13 6.5

Google Trends: Opportunities and limitations in

health and health policy research

Arora, Vishal S.; McKee, Martin; Stuckler,

David

HEALTH POLICY 2019 123 3 13 6.5

Drivers of disruption? Estimating the Uber effect Berger, Thor; Chen, Chinchih; Frey, Carl

Benedikt

EUROPEAN

ECONOMIC REVIEW

2018 110

13 4.33

The Charlie Sheen Effect on Rapid In-home

Human Immunodeficiency Virus Test Sales

Allem, Jon-Patrick; Leas, Eric C.; Caputi,

Theodore L.; Dredze, Mark; Althouse,

PREVENTION

SCIENCE

2017 18 5 13 3.25

66


s

Cit./Year

Benjamin M.; Noar, Seth M.; Ayers, John

W.

Trending now: Using big data to examine public

opinion of space policy

Cobb, Wendy N. Whitman SPACE POLICY 2015 32

13 2.17

Monitoring public interest toward pertussis

outbreaks: an extensive Google Trends analysis

Gianfredi, V; Bragazzi, N. L.; Mahamid,

M.; Bisharat, B.; Mahroum, N.; Amital, H.;

Adawi, M.

PUBLIC HEALTH 2018 165

12 4

Using Search Engine Data as a Tool to Predict

Syphilis

Young, Sean D.; Torrone, Elizabeth A.;

Urata, John; Aral, Sevgi O.

EPIDEMIOLOGY 2018 29 4 12 4

Google Trends as a Resource for Informing Plastic

Surgery Marketing Decisions

Ward, Brittany; Ward, Max; Paskhover,

Boris

AESTHETIC PLASTIC

SURGERY

2018 42 2 12 4

Internet-based monitoring of public perception of

conservation

Soriano-Redondo, Andrea; Bearhop, Stuart;

Lock, Leigh; Votier, Stephen C.; Hilton,

Geoff M.

BIOLOGICAL

CONSERVATION

2017 206

12 3

An audit of the quality of online immunisation

information available to Australian parents

Wiley, K. E.; Steffens, M.; Berry, N.;

Leask, J.

BMC PUBLIC

HEALTH

2017 17

12 3

Tracking search engine queries for suicide in the

United Kingdom, 2004-2013

Arora, V. S.; Stuckler, D.; McKee, M. PUBLIC HEALTH 2016 137

12 2.4

Google's MIDAS Touch: Predicting UK

Unemployment with Internet Search Data

Smith, Paul JOURNAL OF

FORECASTING

2016 35 3 12 2.4

Effect of Tobacco Control Policies on Information

Seeking for Smoking Cessation in the Netherlands:

A Google Trends Study

Troelstra, Sigrid A.; Bosdriesz, Jizzo R.; De

Boer, Michiel R.; Kunst, Anton E.

PLOS ONE 2016 11 2 12 2.4

CITES-listings, EU eel trade bans and the increase

of export of tropical eels out of Indonesia

Nijman, Vincent MARINE POLICY 2015 58

12 2

Forecasting Tourism Demand with Decomposed

Search Cycles

Li, Xin; Law, Rob JOURNAL OF

TRAVEL RESEARCH

2020 59 1 11 11

Spurious patterns in Google Trends data - An

analysis of the effects on tourism demand

forecasting in Germany

Bokelmann, Bjoern; Lessmann, Stefan TOURISM

MANAGEMENT

2019 75

11 5.5

Media attention and Bitcoin prices Philippas, Dionisis; Rjiba, Hatem; Guesmi,

Khaled; Goutte, Stephane

FINANCE RESEARCH

LETTERS

2019 30

11 5.5

Forecasting sales in the supply chain: Consumer

analytics in the big data era

Boone, Tonya; Ganeshan, Ram; Jain,

Aditya; Sanders, Nada R.

INTERNATIONAL

JOURNAL OF

FORECASTING

2019 35 1 11 5.5

The cross-correlations between online sentiment

proxies: Evidence from Google Trends and Twitter

Zhang, Zuochao; Zhang, Yongjie; Shen,

Dehua; Zhang, Wei

PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2018 508

11 3.67

Combining official and Google Trends data to

forecast the Italian youth unemployment rate

Naccarato, Alessia; Falorsi, Stefano;

Loriga, Silvia; Pierini, Andrea

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2018 130

11 3.67

67


s

Cit./Year

The Google voter: search engines and elections in

the new media ecology

Trevisan, Filippo; Hoskins, Andrew; Oates,

Sarah; Mahlouly, Dounia

INFORMATION

COMMUNICATION &

SOCIETY

2018 21 1 11 3.67

Income Inequality, Income, and Internet Searches

for Status Goods: A Cross-National Study of the

Association Between Inequality and Well-Being

Walasek, Lukasz; Brown, Gordon D. A. SOCIAL

INDICATORS

RESEARCH

2016 129 3 11 2.2

Searching Choices: Quantifying Decision-Making

Processes Using Search Engine Data

Moat, Helen Susannah; Olivola,

Christopher Y.; Chater, Nick; Preis, Tobias

TOPICS IN

COGNITIVE

SCIENCE

2016 8 3 11 2.2

Brief Report: Trends in US National Autism

Awareness from 2004 to 2014: The Impact of

National Autism Awareness Month

DeVilbiss, Elizabeth A.; Lee, Brian K. JOURNAL OF

AUTISM AND

DEVELOPMENTAL

DISORDERS

2014 44 12 11 1.57

Web search query volume as a measure of

pharmaceutical utilization and changes in

prescribing patterns

Simmering, Jacob E.; Polgreen, Linnea A.;

Polgreen, Philip M.

RESEARCH IN

SOCIAL &

ADMINISTRATIVE

PHARMACY

2014 10 6 11 1.57

Discovering business information from search

engine query data

Vaughan, Liwen ONLINE

INFORMATION

REVIEW

2014 38 4 11 1.57

Considering connections between Hollywood and

biodiversity conservation

Silk, Matthew J.; Crowley, Sarah L.;

Woodhead, Anna J.; Nuno, Ana

CONSERVATION

BIOLOGY

2018 32 3 10 3.33

In search of the determinants of European asset

market comovements

Gomes, Pedro; Taamouti, Abderrahim INTERNATIONAL

REVIEW OF

ECONOMICS &

FINANCE

2016 44

10 2

New Internet search volume-based weighting

method for integrating various environmental

impacts

Ji, Changyoon; Hong, Taehoon ENVIRONMENTAL

IMPACT

ASSESSMENT

REVIEW

2016 56

10 2

Big Data under the Microscope and Brains in

Social Context: Integrating Methods from

Computational Social Science and Neuroscience

O'Donnell, Matthew Brook; Falk, Emily B. ANNALS OF THE

AMERICAN

ACADEMY OF

POLITICAL AND

SOCIAL SCIENCE

2015 659 1 10 1.67

THE 2007-2008 US RECESSION: WHAT DID

THE REAL-TIME GOOGLE TRENDS DATA

TELL THE UNITED STATES?

Chen, Tao; So, Erin Pik Ki; Wu, Liang;

Yan, Isabel Kit Ming

CONTEMPORARY

ECONOMIC POLICY

2015 33 2 10 1.67

A method for automatic generation of fuzzy

membership functions for mobile device's

characteristics based on Google Trends

Almeida, Aitor; Orduna, Pablo; Castillejo,

Eduardo; Lopez-de-Ipina, Diego; Sacristan,

Marcos

COMPUTERS IN

HUMAN BEHAVIOR

2013 29 2 10 1.25

68


s

Cit./Year

Exploring the predictive ability of LIKES of posts

on the Facebook pages of four major city DMOs in

Austria

Gunter, Ulrich; Oender, Irem; Gindl, Stefan TOURISM

ECONOMICS

2019 25 3 9 4.5

Demand forecasting with user-generated online

information

Schaer, Oliver; Kourentzes, Nikolaos;

Fildes, Robert

INTERNATIONAL

JOURNAL OF

FORECASTING

2019 35 1 9 4.5

Using four different online media sources to

forecast the crude oil price

Elshendy, Mohammed; Colladon, Andrea

Fronzetti; Battistoni, Elisa; Gloor, Peter A.

JOURNAL OF

INFORMATION

SCIENCE

2018 44 3 9 3

Google Trends and the forecasting performance of

exchange rate models

Bulut, Levent JOURNAL OF

FORECASTING

2018 37 3 9 3

News trends and web search query of HIV/AIDS in

Hong Kong

Chiu, Alice P. Y.; Lin, Qianying; He,

Daihai

PLOS ONE 2017 12 9 9 2.25

Does microblogging convey firm-specific

information? Evidence from China

Shen, Dehua; Li, Xiao; Xue, Mei; Zhang,

Wei

PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2017 482

9 2.25

Can We Predict the Financial Markets Based on

Google's Search Queries?

Perlin, Marcelo S.; Caldeira, Joao F.;

Santos, Andre A. P.; Pontuschka, Martin

JOURNAL OF

FORECASTING

2017 36 4 9 2.25

Investor attention and the expected returns of reits Yung, Kenneth; Nafar, Nadia INTERNATIONAL

REVIEW OF

ECONOMICS &

FINANCE

2017 48

9 2.25

The half-life of a teachable moment: The case of

Nobel laureates

Baram-Tsabari, Ayelet; Segev, Elad PUBLIC

UNDERSTANDING

OF SCIENCE

2015 24 3 9 1.5

Health and well-being in the great recession Askitas, Nikolaos; Zimmermann, Klaus F. INTERNATIONAL

JOURNAL OF

MANPOWER

2015 36 1 9 1.5

Conspiratory fascination versus public interest: the

case of 'climategate'

Lewandowsky, Stephan ENVIRONMENTAL

RESEARCH LETTERS

2014 9 11 9 1.29

Who are interested in online science simulations?

Tracking a trend of digital divide in Internet use

Zhang, Meilan COMPUTERS &

EDUCATION

2014 76

9 1.29

Placing Wikimapia: an exploratory analysis Ballatore, Andrea; Arsanjani, Jamal Jokar INTERNATIONAL

JOURNAL OF

GEOGRAPHICAL

INFORMATION

SCIENCE

2019 33 8 8 4

Does Google search index really help predicting

stock market volatility? Evidence from a modified

mixed data sampling model on volatility

Xu, Qifa; Bo, Zhongpu; Jiang, Cuixia; Liu,

Yezheng

KNOWLEDGE-

BASED SYSTEMS

2019 166

8 4

Correlation between Google Trends on dengue

fever and national surveillance report in Indonesia

Husnayain, Atina; Fuad, Anis; Lazuardi,

Lutfan

GLOBAL HEALTH

ACTION

2019 12 1 8 4

69


s

Cit./Year

News coverage, digital activism, and geographical

saliency: A case study of refugee camps and

volunteered geographical information

Mahabir, Ron; Croitoru, Arie; Crooks,

Andrew; Agouris, Peggy; Stefanidis,

Anthony

PLOS ONE 2018 13 11 8 2.67

Harnessing Big Data for Communicable Tropical

and Sub-Tropical Disorders: Implications From a

Systematic Review of the Literature

Gianfredi, Vincenza; Bragazzi, Nicola

Luigi; Nucci, Daniele; Martini, Mariano;

Rosselli, Roberto; Minelli, Liliana; Moretti,

Massimo

FRONTIERS IN

PUBLIC HEALTH

2018 6

8 2.67

Analysis of New Technology Trends in Education:

2010-2015

Martin, Sergio; Lopez-Martin, Esther;

Lopez-Rey, Africa; Cubillo, Joaquin;

Moreno-Pulido, Alexis; Castro, Manuel

IEEE ACCESS 2018 6

8 2.67

Information Flow in the 21st Century: The

Dynamics of Agenda-Uptake

Gruszczynski, Mike; Wagner, Michael W. MASS

COMMUNICATION

AND SOCIETY

2017 20 3 8 2

Forecasting by analogy using the web search traffic Jun, Seung-Pyo; Sung, Tae-Eung; Park,

Hyun-Woo

TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2017 115

8 2

Visualization of brand positioning based on

consumer web search information Using social

network analysis

Jun, Seung-Pyo; Park, Do-Hyung INTERNET

RESEARCH

2017 27 2 8 2

Measuring social influence for firm-level financial

performance

Luo, Peng; Chen, Kun; Wu, Chong ELECTRONIC

COMMERCE

RESEARCH AND

APPLICATIONS

2016 20

8 1.6

Explaining bank stock performance with crisis

sentiment

Irresberger, Felix; Muehlnickel, Janina;

Weiss, Gregor N. F.

JOURNAL OF

BANKING &

FINANCE

2015 59

8 1.33

Forecasting tourism demand using search query

data: A hybrid modelling approach

Wen, Long; Liu, Chang; Song, Haiyan TOURISM

ECONOMICS

2019 25 3 7 3.5

Monitoring of Drought Awareness from Google

Trends: A Case Study of the 2011-17 California

Drought

Kam, Jonghun; Stowers, Kimberly; Kim,

Sungyoon

WEATHER CLIMATE

AND SOCIETY

2019 11 2 7 3.5

Public perception of the vegetative

state/unresponsive wakefulness syndrome: a

crowdsourced study

Kondziella, Daniel; Cheung, Man Cheung;

Dutta, Anirban

PEERJ 2019 7

7 3.5

Income Inequality and White-on-Black Racial Bias

in the United States: Evidence From Project

Implicit and Google Trends

Connor, Paul; Sarafidis, Vasilis; Zyphur,

Michael J.; Keltner, Dacher; Chen, Serena

PSYCHOLOGICAL

SCIENCE

2019 30 2 7 3.5

Demand for MOOC - An Application of Big Data Tong, Tingting; Li, Haizheng CHINA ECONOMIC

REVIEW

2018 51

7 2.33

The promise and challenges of new datasets for

accounting research

Teoh, Siew Hong ACCOUNTING

ORGANIZATIONS

AND SOCIETY

2018 68-

69

7 2.33

70


s

Cit./Year

Google Trends and cycles of public interest in

biodiversity: the animal spirits effect

Troumbis, Andreas Y. BIODIVERSITY AND

CONSERVATION

2017 26 14 7 1.75

Societal Influence on Diffusion of Green Buildings:

A Count. Regression Approach

Braun, Thomas; Cajias, Marcelo;

Hohenstatt, Ralf

JOURNAL OF REAL

ESTATE RESEARCH

2017 39 1 7 1.75

Heat stroke internet searches can be a new

heatwave health warning surveillance indicator

Li, Tiantian; Ding, Fan; Sun, Qinghua;

Zhang, Yi; Kinney, Patrick L.

SCIENTIFIC

REPORTS

2016 6

7 1.4

A study on the effects of the CAFE standard on

consumers

Jun, Seung-Pyo; Yoo, Hyoung Sun; Kim,

Ji-Hui

ENERGY POLICY 2016 91

7 1.4

Googling environmental issues Web search queries

as a measurement of public attention on

environmental issues

Qin, Jie; Peng, Tai-Quan INTERNET

RESEARCH

2016 26 1 7 1.4

A potential way of enquiry into human curiosity Guo, Shesen; Zhang, Ganzhou; Zhai, Run BRITISH JOURNAL

OF EDUCATIONAL

TECHNOLOGY

2010 41 3 7 0.64

Supply chain digitisation trends: An integration of

knowledge management

Schniederjans, Dara G.; Curado, Carla;

Khalajhedayati, Mehrnaz

INTERNATIONAL

JOURNAL OF

PRODUCTION

ECONOMICS

2020 220

6 6

Price clustering and sentiment in bitcoin Baig, Ahmed; Blau, Benjamin M.; Sabah,

Nasim

FINANCE RESEARCH

LETTERS

2019 29

6 3

Public Health Implications of Google Searches for

Sunscreen, Sunburn, Skin Cancer, and Melanoma

in the United States

Hopkins, Zachary H.; Secrest, Aaron M. AMERICAN

JOURNAL OF

HEALTH

PROMOTION

2019 33 4 6 3

Sci-Hub: The new and ultimate disruptor? View

from the front

Nicholas, David; Boukacem-Zeghmouri,

Cherifa; Xu, Jie; Herman, Eti; Clark,

David; Abrizah, Abdullah; Rodriguez-

Bravo, Blanca; Swigon, Marzena

LEARNED

PUBLISHING

2019 32 2 6 3

Developing a global indicator for Aichi Target 1 by

merging online data sources to measure

biodiversity awareness and engagement

Cooper, Matthew W.; Di Minin, Enrico;

Hausmann, Anna; Qin, Siyu; Schwartz,

Aaron J.; Correia, Ricardo Aleixo

BIOLOGICAL

CONSERVATION

2019 230

6 3

Digital behavior surveillance: Monitoring dental

caries and toothache interests of Google users from

developing countries

Cruvinel, Thiago; Ayala Aguirre, Patricia

E.; Lotto, Matheus; Oliveira, Thais

Marchini; Rios, Daniela; Pereira Cruvinel,

Agnes F.

ORAL DISEASES 2019 25 1 6 3

The effect of interest in renewable energy on US

household electricity consumption: An analysis

using Google Trends data

Park, Sungjun; Kim, Jinsoo RENEWABLE

ENERGY

2018 127

6 2

Can Big Data Predict the Rise of Novel Drug

Abuse?

Perdue, Robert Todd; Hawdon, James;

Thames, Kelly M.

JOURNAL OF DRUG

ISSUES

2018 48 4 6 2

Can Google econometrics predict unemployment?

Evidence from Spain

Gonzalez-Fernandez, Marcos; Gonzalez-

Velasco, Carmen

ECONOMICS

LETTERS

2018 170

6 2

71


s

Cit./Year

The burden of attention: CEO publicity and tax

avoidance

Duan, Tinghua; Ding, Rong; Hou,

Wenxuan; Zhang, John Ziyang

JOURNAL OF

BUSINESS

RESEARCH

2018 87

6 2

What Can Google Inform Us about People's

Interests regarding Dental Caries in Different

Populations?

Aguirre, Patricia Estefania; Coelho, Melina;

Oliveira, Thais; Rios, Daniela; Cruvinel,

Agnes Fatima; Cruvinel, Thiago

CARIES RESEARCH 2018 52 3 6 2

Perspectives Measles, social media and surveillance

in Baltimore City

Warren, Katherine E.; Wen, Leana S. JOURNAL OF

PUBLIC HEALTH

2017 39 3 6 1.5

How to measure public demand for policies when

there is no appropriate survey data?

Oehl, Bianca; Schaffer, Lena Maria;

Bernauer, Thomas

JOURNAL OF

PUBLIC POLICY

2017 37 2 6 1.5

Quantifying the effect of investors' attention on

stock market

Yang, Zhen-Hua; Liu, Jian-Guo; Yu,

Chang-Rui; Han, Jing-Ti

PLOS ONE 2017 12 5 6 1.5

The More You Know, the More You Can Grow:

An Information Theoretic Approach to Growth in

the Information Age

Hilbert, Martin ENTROPY 2017 19 2 6 1.5

Stock return and volatility reactions to information

demand and supply

Moussa, Faten; Delhoumi, Ezzeddine; Ben

Ouda, Olfa

RESEARCH IN

INTERNATIONAL

BUSINESS AND

FINANCE

2017 39

6 1.5

Using Twitter sentiment and emotions analysis of

Google Trends for decisions making

D'Avanzo, Ernesto; Pilato, Giovanni;

Lytras, Miltiadis

PROGRAM-

ELECTRONIC

LIBRARY AND

INFORMATION

SYSTEMS

2017 51 3 6 1.5

Linking Annual Prescription Volume of

Antidepressants to Corresponding Web Search

Query Data A Possible Proxy for Medical

Prescription Behavior?

Gahr, Maximilian; Uzelac, Zeljko; Zeiss,

Rene; Connemann, Bernhard J.; Lang,

Dirk; Schoenfeldt-Lecuona, Carlos

JOURNAL OF

CLINICAL

PSYCHOPHARMACO

LOGY

2015 35 6 6 1

Using Google Trends to estimate the incidence of

influenza-like illness in Argentina

Wenceslao Orellano, Pablo; Itati Reynoso,

Julieta; Antman, Julian; Argibay, Osvaldo

CADERNOS DE

SAUDE PUBLICA

2015 31 4 6 1

Web-search trends shed light on the nature of

lunacy: Relationship between moon phases and

epilepsy information-seeking behavior

Otte, Willem M.; van Diessen, Eric; Bell,

Gail S.; Sander, Josemir W.

EPILEPSY &

BEHAVIOR

2013 29 3 6 0.75

Evaluating state tourism websites using Search

Engine Optimization tools

Vyas, Chaitanya TOURISM

MANAGEMENT

2019 73

5 2.5

Perceived pollution and inbound tourism for

Shanghai: a panel VAR approach

Xu, Xu; Reed, Markum CURRENT ISSUES IN

TOURISM

2019 22 5 5 2.5

Rise to fame: events, media activity and public

interest in pangolins and pangolin trade, 2005-2016

Harrington, Lauren A.; D'Cruze, Neil;

Macdonald, David W.

NATURE

CONSERVATION-

BULGARIA

2018

30 5 1.67

Using Google Trends to explore the New Zealand

public's interest in bariatric surgery

Rahiri, Jamie-Lee; Barazanchi, Ahmed;

Furukawa, Sai; MacCormick, Andrew D.;

Harwood, Matire; Hill, Andrew G.

ANZ JOURNAL OF

SURGERY

2018 88 12 5 1.67

72


s

Cit./Year

Internet searches for opioids predict future

emergency department heroin admissions

Young, Sean D.; Zheng, Kai; Chu, Larry F.;

Humphreys, Keith

DRUG AND

ALCOHOL

DEPENDENCE

2018 190

5 1.67

Forecasting casino revenue by incorporating

Google trends

Kim, Woo-Hyuk; Malek, Kristin INTERNATIONAL

JOURNAL OF

TOURISM

RESEARCH

2018 20 4 5 1.67

Dealing With Information Overload in Multifaceted

Personal Informatics Systems

Jones, Simon L.; Kelly, Ryan HUMAN-COMPUTER

INTERACTION

2018 33 1 5 1.67

Zika pandemic online trends, incidence and health

risk communication: a time trend study

Adebayo, Gbenga; Neumark, Yehuda;

Gesser-Edelsburg, Anat; Abu Ahmad,

Wiessam; Levine, Hagai

BMJ GLOBAL

HEALTH

2017 2 3 5 1.25

Psychogenic non-epileptic seizures (PNES) on the

Internet: Online representation of the disorder and

frequency of search terms

Myers, Lorna; Jones, Jace; Boesten,

Nadine; Lancman, Marcelo

SEIZURE-EUROPEAN

JOURNAL OF

EPILEPSY

2016 40

5 1

The Influence of Weather on Interest in a Sun, Sea,

and Sand Tourist Destination: The Case of Majorca

Rossello, Jaume; Waqas, Aon WEATHER CLIMATE

AND SOCIETY

2016 8 2 5 1

The Role of the Networked Public Sphere in the US

Net Neutrality Policy Debate

Faris, Robert; Roberts, Hal; Etling, Bruce;

Othman, Dalia; Benkler, Yochai

INTERNATIONAL

JOURNAL OF

COMMUNICATION

2016 10

5 1

Nowcasting Intraseasonal Recreational Fishing

Harvest with Internet Search Volume

Carter, David W.; Crosson, Scott; Liese,

Christopher

PLOS ONE 2015 10 9 5 0.83

A semantic network analysis of technological

innovation in dentistry: a case of CAD/CAM

Kim, Jang Hyun; Lee, Jinsuk ASIAN JOURNAL OF

TECHNOLOGY

INNOVATION

2015 23

5 0.83

A multi-scale method for forecasting oil price with

multi-factor search engine data

Tang, Ling; Zhang, Chengyuan; Li, Ling;

Wang, Shouyang

APPLIED ENERGY 2020 257

4 4

Public Attention to a Local Disaster Versus

Competing Focusing Events: Google Trends

Analysis Following the 2016 Louisiana Flood

Yeo, Jungwon; Knox, Claire Connolly SOCIAL SCIENCE

QUARTERLY

2019 100 7 4 2

The effect of college education on intolerance:

evidence from Google search data

Chan, Jeff APPLIED

ECONOMICS

LETTERS

2019 26 2 4 2

Forecasting Hospital Emergency Department

Patient Volume Using Internet Search Data

Ho, Andrew Fu Wah; To, Bryan Zhan Yuan

Se; Koh, Jin Ming; Cheong, Kang Hao

IEEE ACCESS 2019 7

4 2

Trump's Effect on stock markets: A multiscale

approach

de Area Leao Pereira, Eder Johnson; da

Silva, Marcus Fernandes; da Cunha Lima, I.

C.; Pereira, H. B. B.

PHYSICA A-

STATISTICAL

MECHANICS AND

ITS APPLICATIONS

2018 512

4 1.33

Open data mining for Taiwan's dengue epidemic Wu, ChienHsing; Kao, Shu-Chen; Shih,

Chia-Hung; Kan, Meng-Hsuan

ACTA TROPICA 2018 183

4 1.33

73


s

Cit./Year

Geospatial Distribution of Local Health

Department Tweets and Online Searches About

Ebola During the 2014 Ebola Outbreak

Wong, Roger; Harris, Jenine K. DISASTER

MEDICINE AND

PUBLIC HEALTH

PREPAREDNESS

2018 12 3 4 1.33

Framing Suicide - Investigating the News Media

and Public's Use of the Problematic Suicide

Referents Freitod and Selbstmord in German-

Speaking Countries

Arendt, Florian CRISIS-THE

JOURNAL OF CRISIS

INTERVENTION AND

SUICIDE

PREVENTION

2018 39 1 4 1.33

Tracking internet interest in anabolic-androgenic

steroids using Google Trends

Teck, Joseph Tay Wee; McCann, Mark INTERNATIONAL

JOURNAL OF DRUG

POLICY

2018 51

4 1.33

Using Google trends data to assess public

understanding on the environmental risks

Durmusoglu, Zeynep Didem Unutmaz HUMAN AND

ECOLOGICAL RISK

ASSESSMENT

2017 23 8 4 1

Internet searches and transactions on the Dutch

housing market

van Veldhuizen, Sander; Vogt, Benedikt;

Voogt, Bart

APPLIED

ECONOMICS

LETTERS

2016 23 18 4 0.8

GOVERNMENT PRESS RELEASES AND

CITIZEN PERCEPTIONS OF GOVERNMENT

PERFORMANCE: EVIDENCE FROM GOOGLE

TRENDS DATA

Hong, Sounman PUBLIC

PERFORMANCE &

MANAGEMENT

REVIEW

2016 39 4 4 0.8

Open source data reveals connection between

online and on-street protest activity

Qi, Hong; Manrique, Pedro; Johnson,

Daniela; Restrepo, Elvira; Johnson, Neil F.

EPJ DATA SCIENCE 2016 5

4 0.8

Using newspapers for tracking the business cycle: a

comparative study for Germany and Switzerland

Iselin, David; Siliverstovs, Boriss APPLIED

ECONOMICS

2016 48 12 4 0.8

Evaluation of internet search trends of some

common oral problems, 2004 to 2014

Harorli, O. T.; Harorli, H. COMMUNITY

DENTAL HEALTH

2014 31 3 4 0.57

Has there been an increased interest in smoking

cessation during the first months of the COVID-19

pandemic? A Google Trends study

Heerfordt, C.; Heerfordt, I. M. PUBLIC HEALTH 2020 183

3 3

Data source combination for tourism demand

forecasting

Hu, Mingming; Song, Haiyan TOURISM

ECONOMICS

2019

3 1.5

More Bang for the Buck: Media Coverage of

Suicide Attacks

Jetter, Michael TERRORISM AND

POLITICAL

VIOLENCE

2019 31 4 3 1.5

Is Digital Epidemiology the Future of Clinical

Epidemiology?

Lippi, Giuseppe; Mattiuzzi, Camilla;

Cervellin, Gianfranco

JOURNAL OF

EPIDEMIOLOGY

AND GLOBAL

HEALTH

2019 9 2 3 1.5

Well-being through the lens of the internet Algan, Yann; Murtin, Fabrice; Beasley,

Elizabeth; Higa, Kazuhito; Senik, Claudia

PLOS ONE 2019 14 1 3 1.5

74


s

Cit./Year

Associations of the Stoptober smoking cessation

program with information seeking for smoking

cessation: A Google trends study

Tieks, Alieke; Troelstra, Sigrid A.;

Hoekstra, Trynke; Kunst, Anton E.

DRUG AND

ALCOHOL

DEPENDENCE

2019 194

3 1.5

Terrorism and Wine Tourism: The Case of

Museum Attendance

Gergaud, Olivier; Livat, Florine; Song,

Haiyan

JOURNAL OF WINE

ECONOMICS

2018 13 4 3 1

Marine environmental issues in the mass media:

Insights from television, newspaper and internet

searches in Chile

Thompson-Saud, Gabriela; Gelcich, Stefan;

Barraza, Jose

OCEAN & COASTAL

MANAGEMENT

2018 165

3 1

Nowcasting and forecasting aquaponics by Google

Trends in European countries

Palma Lampreia Dos Santos, Maria Jose TECHNOLOGICAL

FORECASTING AND

SOCIAL CHANGE

2018 134

3 1

Using Internet Search Data to Measure Changes in

Social Perceptions: A Methodology and an

Application

Reyes, Tomas; Majluf, Nicolas; Ibanez,

Ricardo

SOCIAL SCIENCE

QUARTERLY

2018 99 2 3 1

Using Internet Search Trends and Historical

Trading Data for Predicting Stock Markets by the

Least Squares Support Vector Regression Model

Pai, Ping-Feng; Hong, Ling-Chuang; Lin,

Kuo-Ping

COMPUTATIONAL

INTELLIGENCE AND

NEUROSCIENCE

2018

3 1

Has information on suicide methods provided via

the Internet negatively impacted suicide rates?

Paul, Elise; Mergl, Roland; Hegerl, Ulrich PLOS ONE 2017 12 12 3 0.75

Assessing the effectiveness of disease awareness

programs: Evidence from Google Trends data for

the world awareness dates

Shariatpanahi, Seyed Peyman; Jafari,

Afshin; Sadeghipour, Maryam; Azadeh-

Fard, Nasibeh; Majidzadeh-A, Keivan;

Farahmand, Leila; Ansari, Alireza Madjid

TELEMATICS AND

INFORMATICS

2017 34 7 3 0.75

Business ethics searches: A socioeconomic and

demographic analysis of US Google Trends in the

context of the 2008 financial crisis

Faugere, Christophe; Gergaud, Olivier BUSINESS ETHICS-A

EUROPEAN REVIEW

2017 26 3 3 0.75

Fear on the networks: analyzing the 2014 Ebola

outbreak

D'Agostino, Marcelo; Mejia, Felipe;

Brooks, Ian; Marti, Myrna; Novillo-Ortiz,

David; de Cosio, Gerardo

REVISTA

PANAMERICANA DE

SALUD PUBLICA-

PAN AMERICAN

JOURNAL OF

PUBLIC HEALTH

2017 41

3 0.75

Retail investor information demand - speculating

and investing in structured products

Schroff, Sebastian; Meyer, Stephan;

Burghof, Hans-Peter

EUROPEAN

JOURNAL OF

FINANCE

2016 22 11 3 0.6

Oscillatory Patterns in the Amount of Demand for

Dental Visits: An Agent Based Modeling Approach

Sadeghipour, Maryam; Shariatpanahi,

Peyman; Jafari, Afshin; Khosnevisan,

Mohammad Hossein; Ahmady, Arezoo Ebn

JASSS-THE

JOURNAL OF

ARTIFICIAL

SOCIETIES AND

SOCIAL

SIMULATION

2016 19 3 3 0.6

75


s

Cit./Year

Computational Models of Consumer Confidence

from Large-Scale Online Attention Data: Crowd-

Sourcing Econometrics

Dong, Xianlei; Bollen, Johan PLOS ONE 2015 10 3 3 0.5

A review of pornography use research:

Methodology and results from four sources

Gmeiner, Michael; Price, Joseph; Worley,

Michael

CYBERPSYCHOLOG

Y-JOURNAL OF

PSYCHOSOCIAL

RESEARCH ON

CYBERSPACE

2015 9 4 3 0.5

Estimating Population Ecology Models for the

WWW Market: Evidence of Competitive

Oligopolies

Mateos de Cabo, Ruth; Gimeno, Ricardo NONLINEAR

DYNAMICS

PSYCHOLOGY AND

LIFE SCIENCES

2013 17 1 3 0.38

Google trends and the predictability of precious

metals

Salisu, Afees A.; Ogbonna, Ahamuefula E.;

Adewuyi, Adeolu

RESOURCES POLICY 2020 65

2 2

Spatio-temporal dynamics and drivers of public

interest in invasive alien species

Fukano, Yuya; Soga, Masashi BIOLOGICAL

INVASIONS

2019 21 12 2 1

Nowcasting of the US unemployment rate using

Google Trends

Nagao, Shintaro; Takeda, Fumiko; Tanaka,

Riku

FINANCE RESEARCH

LETTERS

2019 30

2 1

The Validity of Google Trends Search Volumes for

Behavioral Forecasting of National Suicide Rates in

Ireland

Barros, Joana M.; Melia, Ruth; Francis,

Kady; Bogue, John; O'Sullivan, Mary;

Young, Karen; Bernert, Rebecca A.;

Rebholz-Schuhmann, Dietrich; Duggan,

Jim

INTERNATIONAL

JOURNAL OF

ENVIRONMENTAL

RESEARCH AND

PUBLIC HEALTH

2019 16 17 2 1

Meningococcal disease in Italy: public concern,

media coverage and policy change

Covolo, Loredana; Croce, Elia; Moneda,

Marco; Zanardini, Elena; Gelatti, Umberto;

Schulz, Peter J.; Ceretti, Elisabetta

BMC PUBLIC

HEALTH

2019 19 1 2 1

Developing an early warning system of suicide

using Google Trends and media reporting

Chai Yi; Luo Hao; Zhang Qingpeng; Cheng

Qijin; Lui, Carrie S. M.; Yip, Paul S. F.

JOURNAL OF

AFFECTIVE

DISORDERS

2019 255

2 1

Spillovers between the oil sector and the S&P500:

The impact of information flow about crude oil

Aromi, Daniel; Clements, Adam ENERGY

ECONOMICS

2019 81

2 1

Investor attention and short-term return reversals Heyman, Dries; Lescrauwaet, Michiel;

Stieperaere, Hannes

FINANCE RESEARCH

LETTERS

2019 29

2 1

Information availability and return volatility in the

bitcoin Market: Analyzing differences of user

opinion and interest

Yu, Ju Hyun; Kang, Juyoung; Park, Sangun INFORMATION

PROCESSING &

MANAGEMENT

2019 56 3 2 1

Tobacco Price Increases and Population Interest in

Smoking Cessation in Japan Between 2004 and

2016: A Google Trends Analysis

Tabuchi, Takahiro; Fukui, Keisuke; Gallus,

Silvano

NICOTINE &

TOBACCO

RESEARCH

2019 21 4 2 1

Forecasting private consumption with Google

Trends data

Woo, Jaemin; Owen, Ann L. JOURNAL OF

FORECASTING

2019 38 2 2 1

76


s

Cit./Year

Nutritional Culturomics and Big Data; Macroscopic

Patterns of Change in Food, Nutrition and Diet

Choices

Troumbis, Andreas Y.; Hatziantoniou,

Maria; Vasios, Georgios K.

CURRENT

PHARMACEUTICAL

BIOTECHNOLOGY

2019 20 10 2 1

Latin American countries lead in Google search

volumes for anorexia nervosa and bulimia nervosa:

Implications for global mental health research

Eli, Karin INTERNATIONAL

JOURNAL OF

EATING DISORDERS

2018 51 12 2 0.67

Measuring the impact of monetary policy attention

on global asset volatility using search data

Wohlfarth, Paul ECONOMICS

LETTERS

2018 173

2 0.67

GOOGLE SEARCH EFFECT ON EXPERIENCE

PRODUCT SALES AND USERS' MOTIVATION

TO SEARCH: EMPIRICAL EVIDENCE FROM

THE HOTEL INDUSTRY

Zhao, Daying; Fang, Bin; Li, Huiying; Ye,

Qiang

JOURNAL OF

ELECTRONIC

COMMERCE

RESEARCH

2018 19 4 2 0.67

Clustering functional data streams: Unsupervised

classification of soccer top players based on Google

trends

Fortuna, Francesca; Maturo, Fabrizio; Di

Battista, Tonio

QUALITY AND

RELIABILITY

ENGINEERING

INTERNATIONAL

2018 34 7 2 0.67

Comparison between Bayesian and information-

theoretic model averaging: Fossil fuels prices

example

Drachal, Krzysztof ENERGY

ECONOMICS

2018 74

2 0.67

Mining online activity data to understand food

consumption behavior: A case of Asian fish sauce

among Japanese consumers

Nakano, Mitsutoshi; Sato, Hiroaki;

Watanabe, Toshihiro; Takano, Katsumi;

Sagane, Yoshimasa

FOOD SCIENCE &

NUTRITION

2018 6 4 2 0.67

Trigger warning: the causal impact of gun

ownership on suicide

Vitt, David C.; McQuoid, Alexander F.;

Moore, Charles; Sawyer, Stephen

APPLIED

ECONOMICS

2018 50 53 2 0.67

What did Ryan Lochte do? The double-edged

sword of endorsers behaving badly

Vredenburg, Jessica; Giroux, Marilyn INTERNATIONAL

JOURNAL OF

SPORTS

MARKETING &

SPONSORSHIP

2018 19 3 2 0.67

Comparison of Periodic Behavior of Consumer

Online Searches for Restaurants in the US and

China Based on Search Engine Data

Tang, Hao; Qiu, Yichen; Guo, Ya; Liu,

Juan

IEEE ACCESS 2018 6

2 0.67

Big Data: An Institutional Perspective on

Opportunities and Challenges

Hasnat, Baban JOURNAL OF

ECONOMIC ISSUES

2018 52 2 2 0.67

Search and You Shall Find: Geographic

Characteristics Associated With Google Searches

During the Affordable Care Act's First Enrollment

Period

Gollust, Sarah E.; Qin, Xuanzi; Wilcock,

Andrew D.; Baum, Laura M.; Barry,

Colleen L.; Niederdeppe, Jeff; Fowler,

Erika Franklin; Karaca-Mandic, Pinar

MEDICAL CARE

RESEARCH AND

REVIEW

2017 74 6 2 0.5

Evaluating the use of internet search volumes for

time series modeling of sales in the video game

industry

Ruohonen, Jukka; Hyrynsalmi, Sami ELECTRONIC

MARKETS

2017 27 4 2 0.5

Use of enhancement drugs amongst athletes and

television celebrities and public interest in

Avilez, J. L.; Zevallos-Morales, A.; Taype-

Rondan, A.

PUBLIC HEALTH 2017 146

2 0.5

77


s

Cit./Year

androgenic anabolic steroids. Exploring two

Peruvian cases with Google Trends

Periodicity analysis and a model structure for

consumer behavior on hotel online search interest

in the US

Liu, Juan; Li, Xue; Guo, Ya INTERNATIONAL

JOURNAL OF

CONTEMPORARY

HOSPITALITY

MANAGEMENT

2017 29 5 2 0.5

Predicting Virtual World User Population

Fluctuations with Deep Learning

Kim, Young Bin; Park, Nuri; Zhang,

Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim,

Chang Hun

PLOS ONE 2016 11 12 2 0.4

Abortion Internet searches. An evaluation with

Google Trends in Peru

Jesus Tejada-Llacsa, Paul GACETA SANITARIA 2016 30 4 2 0.4

Content and quality of websites supporting self-

management of chronic breathlessness in advanced

illness: a systematic review

Luckett, Tim; Disler, Rebecca; Hosie,

Annmarie; Johnson, Miriam; Davidson,

Patricia; Currow, David; Sumah, Anthony;

Phillips, Jane

NPJ PRIMARY CARE

RESPIRATORY

MEDICINE

2016 26

2 0.4

Reforming Higher Education Finance in Turkey:

The Alumni - Crowdfunded Student Debt Fund A-

CSDF Model

Son-Turan, Semen EGITIM VE BILIM-

EDUCATION AND

SCIENCE

2016 41 184 2 0.4

Discovering the unequal interest in popular online

educational games and its implications: A case

study

Zhang, Meilan BRITISH JOURNAL

OF EDUCATIONAL

TECHNOLOGY

2016 47 2 2 0.4

Presidential Attention Focusing in the Global

Arena: The Impact of International Travel on

Foreign Publics

Cohen, Jeffrey E. PRESIDENTIAL

STUDIES

QUARTERLY

2016 46 1 2 0.4

After the crisis? Big data and the methodological

challenges of empirical sociology

Burrows, R.; Savage, M. SOTSIOLOGICHESKI

E ISSLEDOVANIYA

2016

3 2 0.4

Global search demand for varicose vein

information on the internet

El-Sheikha, Joseph PHLEBOLOGY 2015 30 8 2 0.33

An informetrics view of the relationship between

internet ethics, computer ethics and cyberethics

Onyancha, Omwoyo Bosire LIBRARY HI TECH 2015 33 3 2 0.33

Forecasting tourism demand with multisource big

data

Li, Hengyun; Hu, Mingming; Li, Gang ANNALS OF

TOURISM

RESEARCH

2020 83

1 1

More effective strategies are required to strengthen

public awareness of COVID-19: Evidence from

Google Trends

Hu, Dingtao; Lou, Xiaoqi; Xu, Zhiwei;

Meng, Nana; Xie, Qiaomei; Zhang, Man;

Zou, Yanfeng; Liu, Jiatao; Sun, Guoping;

Wang, Fang

JOURNAL OF

GLOBAL HEALTH

2020 10 1 1 1

Infection prediction using physiological and social

data in social environments

Baldominos, Alejandro; Ogul, Hasan;

Colomo-Palacios, Ricardo; Sanz-Moreno,

Jose; Manuel Gomez-Pulido, Jose

INFORMATION

PROCESSING &

MANAGEMENT

2020 57 3 1 1

Heat-health vulnerability in temperate climates:

lessons and response options from Ireland

Paterson, Shona K.; Godsmark, Christie

Nicole

GLOBALIZATION

AND HEALTH

2020 16 1 1 1

78


s

Cit./Year

The Impact of Widely Publicized Suicides on

Search Trends: Using Google Trends to Test the

Werther and Papageno Effects

Gunn, John F., III; Goldstein, Sara E.;

Lester, David

ARCHIVES OF

SUICIDE RESEARCH

2020 24

1 1

Using Google Trends and Baidu Index to analyze

the impacts of disaster events on company stock

prices

Liu, Ying; Peng, Geng; Hu, Lanyi; Dong,

Jichang; Zhang, Qingqing

INDUSTRIAL

MANAGEMENT &

DATA SYSTEMS

2020 120 2 1 1

What Does Google Trends Tell Us about the

Impact of Brexit on the Unemployment Rate in the

UK?

Simionescu, Mihaela; Streimikiene, Dalia;

Strielkowski, Wadim

SUSTAINABILITY 2020 12 3 1 1

Dr. Google, I am in Pain-Global Internet Searches

Associated with Pain: A Retrospective Analysis of

Google Trends Data

Kaminski, Mikolaj; Loniewski, Igor;

Marlicz, Wojciech

INTERNATIONAL

JOURNAL OF

ENVIRONMENTAL

RESEARCH AND

PUBLIC HEALTH

2020 17 3 1 1

Search trends preceding increases in suicide: A

cross-correlation study of monthly Google search

volume and suicide rate using transfer function

models

Lee, Joo-Young JOURNAL OF

AFFECTIVE

DISORDERS

2020 262

1 1

Responses to mass shooting events The interplay

between the media and the public

Croitoru, Arie; Kien, Sara; Mahabir, Ron;

Radzikowski, Jacek; Crooks, Andrew;

Schuchard, Ross; Begay, Tatyanna; Lee,

Ashley; Bettios, Alex; Stefanidis, Anthony

CRIMINOLOGY &

PUBLIC POLICY

2020 19 1 1 1

Lifestyle Disease Surveillance Using Population

Search Behavior: Feasibility Study

Memon, Shahan Ali; Razak, Saquib;

Weber, Ingmar

JOURNAL OF

MEDICAL INTERNET

RESEARCH

2020 22 1 1 1

Electronic Cigarette Themes on Twitter:

Dissemination Patterns and Relations with Online

News and Search Engine Queries in South Korea

Paek, Hye-Jin; Baek, Hyunmi; Lee,

Saerom; Hove, Thomas

HEALTH

COMMUNICATION

2020 35 1 1 1

Volatility forecasting: the role of internet search

activity and implied volatility

Basistha, Arabinda; Kurov, Alexander;

Wolfe, Marketa

JOURNAL OF RISK

MODEL

VALIDATION

2020 14 1 1 1

EVIDENCE OF PARTISAN AGENDA

FRAGMENTATION IN THE AMERICAN

PUBLIC, 1959-2015

Gruszczynski, Mike PUBLIC OPINION

QUARTERLY

2019 83 4 1 0.5

Google Trends in tourism and hospitality research:

a systematic literature review

Dinis, Gorete; Breda, Zelia; Costa, Carlos;

Pacheco, Osvaldo

JOURNAL OF

HOSPITALITY AND

TOURISM

TECHNOLOGY

2019 10 4 1 0.5

Horizon scanning to identify invasion risk of

ornamental plants marketed in Spain

Bayon, Alvaro; Vila, Montserrat NEOBIOTA 2019

52 1 0.5

Predicting Contagion from the US Financial Crisis

to International Stock Markets Using Dynamic

Copula with Google Trends

Maneejuk, Paravee; Yamaka, Woraphon MATHEMATICS 2019 7 11 1 0.5

79


s

Cit./Year

A novel index of macroeconomic uncertainty for

Turkey based on Google-Trends

Bilgin, Mehmet Huseyin; Demir, Ender;

Gozgor, Giray; Karabulut, Gokhan; Kaya,

Huseyin

ECONOMICS

LETTERS

2019 184

1 0.5

A Review of Public Issue Salience: Concepts,

Determinants and Effects on Voting

Dennison, James POLITICAL STUDIES

REVIEW

2019 17 4 1 0.5

Forecasting tourist arrivals: Google Trends meets

mixed-frequency data

Havranek, Tomas; Zeynalov, Ayaz TOURISM

ECONOMICS

2019

1 0.5

Modelling international monthly tourism demand at

the micro destination level with climate indicators

and web-traffic data

Emili, Silvia; Figini, Paolo; Guizzardi,

Andrea

TOURISM

ECONOMICS

2019

1 0.5

Feeling Is Believing? Evidence From Earthquake

Shaking Experience and Insurance Demand

Lin, Xiao JOURNAL OF RISK

AND INSURANCE

2020 87 2 1 0.5

Bringing forecasting into the future: Using Google

to predict visitation in US national parks

Clark, Matt; Wilkins, Emily J.; Dagan,

Dani T.; Powell, Robert; Sharp, Ryan L.;

Hillis, Vicken

JOURNAL OF

ENVIRONMENTAL

MANAGEMENT

2019 243

1 0.5

Perspective: Global country-by-country response of

public interest in the environment to the papal

encyclical, Laudato Si'

McCallum, Malcolm L. BIOLOGICAL

CONSERVATION

2019 235

1 0.5

Laying the Foundation for a Successful Presidential

Campaign: Public Attention and Fundraising in the

Preprimary Period

Swearingen, C. Douglas SOCIAL SCIENCE

QUARTERLY

2019 100 4 1 0.5

Forecasting with auxiliary information in forecasts

using multivariate singular spectrum analysis

Silva, Emmanuel Sirimal; Hassani,

Hossein; Ghodsi, Mansi; Ghodsi, Zara

INFORMATION

SCIENCES

2019 479

1 0.5

Mechanisms of meme propagation in the

mediasphere: a system dynamics model

Perissi, Ilaria; Falsini, Sara; Bardi, Ugo KYBERNETES 2019 48 1 1 0.5

Collective Perception and Exchange Rates Reed, Markum; Ankouri, Kaotar JOURNAL OF

BEHAVIORAL

FINANCE

2019 20 1 1 0.5

Leap2Trend: A Temporal Word Embedding

Approach for Instant Detection of Emerging

Scientific Trends

Dridi, Amna; Gaber, Mohamed Medhat;

Azad, R. Muhammad Atif; Bhogal, Jagdev

IEEE ACCESS 2019 7

1 0.5

Can search engine data improve accuracy of

demand forecasting for new products? Evidence

from automotive market

Kim, Dongha; Woo, JongRoul; Shin,

Jungwoo; Lee, Jongsu; Kim, Yongdai

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DATA SYSTEMS

2019 119 5 1 0.5

A bibliometric analysis of inventory management

research based on knowledge mapping

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LIBRARY

2019 37 1 1 0.5

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news media, organizational PR, and the public

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REVIEW

2018 44 5 1 0.33

How Does the World Google the Internet, Anxiety,

and Happiness?

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Y BEHAVIOR AND

SOCIAL

NETWORKING

2018 21 9 1 0.33

80


s

Cit./Year

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Increases: Evidence from an Algorithm to Detect

Spikes in Time Series Data

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TOBACCO

RESEARCH

2018 20 6 1 0.33

When do Indians feel hot? Internet searches

indicate seasonality suppresses adaptation to heat

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Velde, Ype

ENVIRONMENTAL

RESEARCH LETTERS

2018 13 5 1 0.33

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Information Flows in Google Trends

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JOURNAL OF

COMMUNICATION

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1 0.33

Co-query volume as a proxy for brand relatedness Cho, Sulah INDUSTRIAL

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INTERNET SEARCHES FOR SUICIDE ITS ASSOCIATION WITH

EPIDEMIOLOGICAL DATA AND

INSIGHTS FOR PREVENTION

PROGRAMS

2018 2018 30

1

An infodemiology study on breast cancer in Iran:

Health information supply versus health

information demand in PubMed and Google Trends

Hosseini, Shohreh Seyyed; Asemi, Asefeh;

Shabani, Ahmad; Sohrabi, Mozafar

Cheshmeh

ELECTRONIC

LIBRARY

2018 36 2 1 0.33

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Information Seeking? A Real-World Test Using

Google Search Data

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R.; LoRusso, Susan

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BROADCASTING &

ELECTRONIC MEDIA

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Did online publishers get it right? Using a

naturalistic search strategy to review cognitive

health promotion content on internet webpages

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E.; Cammer, A.; Seaton, J. X.; Friedrich,

T.; Fick, F.

BMC GERIATRICS 2017 17

1 0.25

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measures of Pakistani and American attention to

drone strikes

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INFORMATION

TECHNOLOGY &

POLITICS

2017 14 1 1 0.25

The life cycle of social media Franses, Philip Hans APPLIED

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LETTERS

2015 22 10 1 0.17

Searching for Czech towns by Google users Horak, Jiri; Ivan, Igor; Navratova, Marketa;

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GEOGRAFIE 2013 118 3 1 0.13

Evaluating the environmental protection strategy of

a printed circuit board manufacturer using a T-w

fuzzy importance performance analysis with

Google Trends

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Ju

EXPERT SYSTEMS

WITH

APPLICATIONS

2020 156

0 0

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contributions to people

Aguilera-Alcala, Natividad; Morales-

Reyes, Zebensui; Martin-Lopez, Berta;

Moleon, Marcos; Antonio Sanchez-Zapata,

Jose

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INDICATORS

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0 0

Which predictor is more predictive for Bitcoin

volatility? And why?

Liang, Chao; Zhang, Yaojie; Li, Xiafei;

Ma, Feng

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JOURNAL OF

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0 0

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s

Cit./Year

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Impact of the COVID-19 Pandemic on Mental

Health: Real-Time Surveillance Using Google

Trends

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Laura M.; Voss, Hallie M.; Easwar,

Sanjana; Gerhart, James I.

PSYCHOLOGICAL

TRAUMA-THEORY

RESEARCH

PRACTICE AND

POLICY

2020 12 6 0 0

COVID-19 outbreak, social response, and early

economic effects: a global VAR analysis of cross-

country interdependencies

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POPULATION

ECONOMICS

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0 0

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economy: Bayesian non-linear evidence for Greece

(2009-2015)

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Paraskeuopoulou, Despoina; Michaelides,

Panayotis G.; Tsionas, Efthymios G.

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JOURNAL OF

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AUTISM AND

DEVELOPMENTAL

DISORDERS

2020

0 0

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Risks and Confirmed Cases of Lyme Disease: An

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Kim, Dohyeong; Maxwell, Sarah; Le,

Quang

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PUBLIC HEALTH

2020 8

0 0

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of sectoral stock returns

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Adediran, Idris

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FORECASTING

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0 0

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Role of the Trade War

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Rangan; Pierdzioch, Christian

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ECONOMICS

2020

0 0

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mental health, politics and violence in the context

of mass shootings

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A.; Mittal, V. A.

PLOS ONE 2020 15 8 0 0

Analyzing online search patterns of music festival

tourists

Montoro-Pons, Juan D.; Cuadrado-Garcia,

Manuel

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ECONOMICS

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0 0

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Growth Using Google Trends

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Montes

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BUSINESS &

ECONOMIC

STATISTICS

2020

0 0

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Trends analysis

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Robert

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RESEARCH LETTERS

2020 15 8 0 0

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2020 16 2 0 0

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engine data

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MECHANICS AND

ITS APPLICATIONS

2020 551

0 0

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s

Cit./Year

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uncertainty about the future currency in a Google

search

Anastasiou, Dimitrios; Drakos,

Konstantinos

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JOURNAL OF

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ECONOMICS

2020

0 0

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Olympic Medal Rankings

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2020

0 0

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During the COVID-19 Crisis

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RESOURCE

ECONOMICS

2020 76 4 0 0

Validation: The Use of Google Trends as an

Alternative Data Source for COVID-19

Surveillance in Indonesia

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Lazuardi, Lutfan; Aryandono, Teguh

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PUBLIC HEALTH

2020

0 0

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COVID-19

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Sherzod B.; Okhunjanov, Botir B.

ENTROPY 2020 22 7 0 0

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attractions

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RESEARCH

2020 83

0 0

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forecasting research

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Shaolong; Wei, Yunjie

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MANAGEMENT

PERSPECTIVES

2020 35

0 0

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trends by Google trends

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Silva, Paulo Roberto; da Silva Santos, Ligia

Amparo

CIENCIA & SAUDE

COLETIVA

2020 25 7 0 0

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Enrico

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JOURNAL OF

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RESEARCH

2020 22 4 0 0

Do economic downturns fuel racial animus? Anderson, D. Mark; Crost, Benjamin; Rees,

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ORGANIZATION

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0 0

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Siddhartha

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INTERACTION AND

COORDINATION

2020 15 3 0 0

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s

Cit./Year

A State-Level Analysis of Mortality and Google

Searches for Pornography: Insight from Life

History Theory

Cheng, Lei; Zhou, Xuan; Wang, Fang;

Xiao, Lijuan

ARCHIVES OF

SEXUAL BEHAVIOR

2020

0 0

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Searches during the Early Stages of the COVID-19

Pandemic

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Niederkrotenthaler, Thomas

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JOURNAL OF

PSYCHIATRY-

REVUE

CANADIENNE DE

PSYCHIATRIE

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0 0

Improving Tourist Arrival Prediction: A Big Data

and Artificial Neural Network Approach

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Matthias; Lexhagen, Maria

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2020

0 0

In search of art: rapid estimates of gallery and

museum visits using Google Trends

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Susannah

EPJ DATA SCIENCE 2020 9 1 0 0

Computational Forecasting Methodology for Acute

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E.; Comas-Garcia, Andreu; Garcia-

Sepulveda, Christian A.

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JOURNAL OF

ENVIRONMENTAL

RESEARCH AND

PUBLIC HEALTH

2020 17 12 0 0

Using Google Trends to assess the impact of global

public health days on online health information

seeking behaviour in Central and South America

Havelka, Eva Maria; Mallen, Christian

David; Shepherd, Thomas Andrew

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GLOBAL HEALTH

2020 10 1 0 0

Consequences of banning commercial solaria in

2016 in Australia

Gordon, Louisa G.; Sinclair, Craig;

Cleaves, Noel; Makin, Jennifer K.;

Rodriguez-Acevedo, Astrid J.; Green,

Adele C.

HEALTH POLICY 2020 124 6 0 0

A content analysis from 153 years of print and

online media shows positive perceptions of the

hellbender salamander follow the conservation

biology

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CONSERVATION

2020 246

0 0

Improving unemployment rate forecasts at regional

level in Romania using Google Trends

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FORECASTING AND

SOCIAL CHANGE

2020 155

0 0

Absolutist Words From Search Volume Data

Predict State-Level Suicide Rates in the United

States

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PSYCHOLOGICAL

SCIENCE

2020 8 4 0 0

The traces of ecotourism in a digital world: spatial

and trend analysis of geotagged photographs on

social media and Google search data for sustainable

development

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Yunwoo

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HOSPITALITY AND

TOURISM

TECHNOLOGY

2020

0 0

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Hadj; Ben Aouicha, Mohamed; Abraham,

Ajith

SCIENTOMETRICS 2020 124 2 0 0

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s

Cit./Year

Shining the light on abortion: Drivers of online

abortion searches across the United States in 2018

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Elizabeth; Hubbard, Alan; Prata, Ndola

PLOS ONE 2020 15 5 0 0

Health insurance and self-assessed health: New

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ECONOMICS

2020 29 9 0 0

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community engagement with police: Evidence from

domestic violence calls in Los Angeles

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Catalina

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URBAN ECONOMICS

2020 117

0 0

Google trends identifying seasons of religious

gathering: applied to investigate the correlation

between crowding and flu outbreak

Elhussein, Mariam; Brahimi, Samiha;

Alreedy, Abdullah; Alqahtani, Mohammed;

Olatunji, Sunday O.

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PROCESSING &

MANAGEMENT

2020 57 3 0 0

A Language-Independent Measurement of

Economic Policy Uncertainty in Eastern European

Countries

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MARKETS FINANCE

AND TRADE

2020 56 5 0 0

Determination of the Popularity of Dietary

Supplements Using Google Search Rankings

Kaminski, Mikolaj; Kregielska-Narozna,

Matylda; Bogdanski, Pawel

NUTRIENTS 2020 12 4 0 0

Enhanced Public Interest in Response to the

Refugee and Healthcare Crises in Greece

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Gourgoulianis, Konstantinos I.

INTERNATIONAL

JOURNAL OF

ENVIRONMENTAL

RESEARCH AND

PUBLIC HEALTH

2020 17 7 0 0

Quantifying the sustainability of Bitcoin and

Blockchain

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ENTERPRISE

INFORMATION

MANAGEMENT

2020

0 0

The Dark Web and cannabis use in the United

States: Evidence from a big data research design

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JOURNAL OF DRUG

POLICY

2020 76

0 0

Forecasting Foreign Exchange Markets Using

Google Trends: Prediction Performance of

Competing Models

Wilcoxson, Jordan; Follett, Lendie; Severe,

Sean

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BEHAVIORAL

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2020

0 0

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Outbreak of COVID-19 in China

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AND HEALTHCARE

POLICY

2020 13

0 0

FORECASTING THE INFLATION RATE IN

POLAND AND US USING DYNAMIC MODEL

AVERAGING (DMA) AND GOOGLE QUERIES

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2020 23 2 0 0

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other international leaders on Google Trends (2016-

2019)

Villa-Gracia, Daniel; Cerdan, Victor PROFESIONAL DE

LA INFORMACION

2020 29 1 0 0

Inequality and Bias in the Demand for and Supply

of News

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QUARTERLY

2020 101 1 0 0

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s

Cit./Year

Online Searching and Social Media to Detect

Alcohol Use Risk at Population Scale

Weitzman, Elissa R.; Magane, Kara M.;

Chen, Po-Hua; Amiri, Hadi; Naimi,

Timothy S.; Wisk, Lauren E.

AMERICAN

JOURNAL OF

PREVENTIVE

MEDICINE

2020 58 1 0 0

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migration with online search keywords

Boehme, Marcus H.; Groeger, Andre;

Stoehr, Tobias

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DEVELOPMENT

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2020 142

0 0

When is prime-time in streaming media platforms

and video-on-demands services? New media

consumption patterns and real-time economy

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COMMUNICATION

2020 35 2 0 0

The secret life of PETs A cross-sectional analysis

of interest in privacy enhancing technologies

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JOURNAL OF

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& MANAGEMENT

2020 43 1 0 0

Internet search query data improve forecasts of

daily emergency department volume

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Bickel, Jonathan; Reis, Ben

JOURNAL OF THE

AMERICAN

MEDICAL

INFORMATICS

ASSOCIATION

2019 26 12 0 0

Rhythmicity of health information behaviour

Utilizing the infodemiology approach to study

temporal patterns and variations

Tana, Jonas; Eirola, Emil; Eriksson-Backa,

Kristina

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MANAGEMENT

2019 71 6 0 0

Moving the Needle in MMA: On the Marginal

Revenue Product of UFC Fighters

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SPORTS ECONOMICS

2020 21 2 0 0

Can google trends improve sales forecasts on a

product level?

Fritzsch, Benjamin; Wenger, Kai;

Sibbertsen, Philipp; Ullmann, Georg

APPLIED

ECONOMICS

LETTERS

2019

0 0

Google Trends and forecasting of influenza

epidemics in Lithuania

Austys, D.; Burneikait, M. EUROPEAN

JOURNAL OF

PUBLIC HEALTH

2019 29

0 0

Tax reforms and Google searches: the case of

Spanish VAT reforms during the great recession

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Melissa; Jesus Sanchez-Fuentes, A.

SERIES-JOURNAL OF

THE SPANISH

ECONOMIC

ASSOCIATION

2019 10 3-4 0 0

Do changes in threat salience predict the moral

content of sermons? The case of Friday Khutbas in

Turkey

Alper, Sinan; Bayrak, Fatih; Us, Elif Oyku;

Yilmaz, Onurcan

EUROPEAN

JOURNAL OF

SOCIAL

PSYCHOLOGY

2020 50 3 0 0

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dynamics

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Lucchetta, Marcella; Arzu, Daniela

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s

Cit./Year

Extreme dependence in investor attention and stock

returns ? consequences for forecasting stock returns

and measuring systemic risk

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FINANCE

2020 20 3 0 0

Filtering and prediction of noisy and unstable

signals: The case of Google Trends data

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FORECASTING

2019

0 0

Public interest in gun control in the USA Niforatos, Joshua D.; Zheutlin, Alexander

R.; Pescatore, Richard M.

INJURY

PREVENTION

2019 25

0 0

ONLINE DATING AND SEXUALLY

TRANSMITTED INFECTIONS IN ENGLAND:

AN ECOLOGICAL STUDY USING GOOGLE

TRENDS DATA

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EPIDEMIOLOGY

AND COMMUNITY

HEALTH

2019 73

0 0

Measuring Christian Religiosity by Google Trends Yeung, Timothy Yu-Cheong REVIEW OF

RELIGIOUS

RESEARCH

2019 61 3 0 0

Measuring Uncertainty for New Zealand Using

Data-Rich Approach

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Benjamin

AUSTRALIAN

ECONOMIC REVIEW

2019 52 3 0 0

Bidding for attention: using google trends to

measure global interest in Olympic bidders

Stow, Hollie; Bason, Tom SPORT IN SOCIETY 2019

0 0

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hyperconcentrated topic news

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ADVANCES

2019 5 8 0 0

A copycat crime meme: Ghost riding the whip Surette, Ray CRIME MEDIA

CULTURE

2019

0 0

Here's Looking at You: Public- Versus Elite-Driven

Models of Presidential Primary Elections

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Elizabeth; Finneran, Kate

SOCIAL SCIENCE

QUARTERLY

2019 100 5 0 0

The Effects of Publicized Suicide Deaths on

Subsequent Suicide Counts in Israel

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SUICIDE RESEARCH

2019 23 3 0 0

Building sectoral job search indices for the United

States

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LETTERS

2019 180

0 0

Illuminating the Sewol Ferry Disaster using the

institutional model of punctuated equilibrium

theory

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JOURNAL

2019 56 2 0 0

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Following Celebrity Suicides

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Fisher, Thomas J.; Hughes, Michael; Smith,

April R.

CYBERPSYCHOLOG

Y BEHAVIOR AND

SOCIAL

NETWORKING

2019 22 6 0 0

Utilizing Google Trends to Measure Public Interest

in Marijuana Before and After Recreational

Legalization in California

Khan, Khalid Salim; Athanasiou, Nicholas;

Burke, Michael

AMERICAN

JOURNAL ON

ADDICTIONS

2019 28 3 0 0

Exploring spatiotemporal heterogeneity in online

travel searches: a local spatial model approach

Xu, Jing; Jin, Cheng GEOGRAFISK

TIDSSKRIFT-DANISH

JOURNAL OF

GEOGRAPHY

2019 119 2 0 0

87


s

Cit./Year

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review of scientific and popular definitions online

Sarrica, Mauro; Brondi, Sonia; Fortunati,

Leopoldina

INFORMATION

TECHNOLOGY &

PEOPLE

2019 33 1 0 0

IMPACT OF BEHAVIOURAL ATTENTION ON

THE HOUSEHOLDS' FOREIGN CURRENCY

SAVINGS AS A RESPONSE TO THE

EXTERNAL MACROECONOMIC SHOCKS

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Korab, Petr

PRAGUE ECONOMIC

PAPERS

2019 28 2 0 0

Leveraging media informatics for the surveillance

and understanding of disease outbreaks

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JOURNAL OF

SCIENCE

2019 115 3-4 0 0

A Comparative Analysis of Worldwide Trends in

the Use of Information and Communications

Technology in Engineering Education


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Castro, Manuel

IEEE ACCESS 2019 7

0 0

Behavioural Attention to Financial Indicators:

Evidence from Google Trends Data

Stejskalova, Jolana FINANCE A UVER-

CZECH JOURNAL OF

ECONOMICS AND

FINANCE

2019 69 5 0 0

A Comparative Analysis of Worldwide Trends in

the Use of Information and Communications

Technology in Engineering Education


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Castro, Manuel

IEEE ACCESS 2019 7

0 0

Valuable information in early sales proxies: The

use of Google search ranks in portfolio

optimization

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FORECASTING

2019 38 1 0 0

Does investor attention to energy stocks exhibit

power law?

Ranjan, Ravi Prakash; Bhattachharyya,

Malay

ENERGY

ECONOMICS

2018 75

0 0

Appetitive information seeking behaviour reveals

robust daily rhythmicity for Internet-based food-

related keyword searches

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Tyler J.

ROYAL SOCIETY

OPEN SCIENCE

2018 5 7 0 0

Replicating Predicting the present with Google

trends by Hyunyoung Choi and Hal Varian (The

Economic Record, 2012)

Coupe, Tom ECONOMICS-THE

OPEN ACCESS

OPEN-ASSESSMENT

E-JOURNAL

2018 12

0 0

Racial climate and homeownership Harris, Timothy F.; Yelowitz, Aaron JOURNAL OF

HOUSING

ECONOMICS

2018 40

0 0

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USING GOOGLE TRENDS: US STATE-

SPECIFIC SEARCH BEHAVIOR

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BEHAVIORAL

MEDICINE

2018 52

0 0

How good can heuristic-based forecasts be? A

comparative performance of econometric and

heuristic models for UK and US asset returns

Guidolin, Massimo; Orlov, Alexei G.;

Pedio, Manuela

QUANTITATIVE

FINANCE

2018 18 1 0 0

Fines versus prison for the issuance of bad checks:

Evidence from a policy shift in Turkey

Eksi, Ozan; Gurdal, Mehmet Y.; Orman,

Cuneyt

JOURNAL OF

ECONOMIC

2017 143

0 0

88


s

Cit./Year

BEHAVIOR &

ORGANIZATION

The use of open source internet to analysis and

predict stock market trading volume

Moussa, Faten; BenOuda, Olfa; Delhoumi,

Ezzeddine

RESEARCH IN

INTERNATIONAL

BUSINESS AND

FINANCE

2017 41

0 0

Utility of Web search query data in testing

theoretical assumptions about mephedrone

Kapitany-Foveny, Mate; Demetrovics,

Zsolt

HUMAN

PSYCHOPHARMACO

LOGY-CLINICAL

AND

EXPERIMENTAL

2017 32 3 0 0

How Does the Internet Pornography Market

Function?

Joos, Richard ZEITSCHRIFT FUR

SEXUALFORSCHUN

G

2017 30 1 0 0

THE DIFFERENCES IN EFFECTS OF

RENAMING SCHIZOPHRENIA AMONG 3

ASIAN COUNTRIES USING GOOGLE TRENDS

Lee, Yu Sang; Kwon, Jun Soo; Hong,

Kyung Sue

SCHIZOPHRENIA

BULLETIN

2017 43

0 0

Mists, vapors and other illusory volatilities of

electronic cigarettes

de Almeida, Liz Maria; da Silva, Rildo

Pereira; Cheriff dos Santos, Antonio Tadeu;

de Andrade, Joecy Dias; Suarez, Maribel

Carvalho

CADERNOS DE

SAUDE PUBLICA

2017 33

0 0

Adverse drug reaction early warning using user

search data

Shang, Wei; Chen, Hsinchun; Livoti,

Christine

ONLINE

INFORMATION

REVIEW

2017 41 4 0 0

Association between volume and momentum of

online searches and real-world collective unrest

Qi, Hong; Manrique, Pedro; Johnson,

Daniela; Restrepo, Elvira; Johnson, Neil F.

Results in Physics 2016 6

0 0

Novel Data Sources for Women's Health Research:

Mapping Breast Screening Information Seeking

Through Google Trends

Dehkordy, Soudabeh Fazeli; Carlos, Ruth;

Hall, Kelli; Dalton, Vanessa

JOURNAL OF

WOMENS HEALTH

2014 23 4 0 0

EARLY 2012 WINNERS AND LOSERS:

ELLISON SAILS, GOOGLE TRENDS

DOWNWARD

[Anonymous] FORBES 2012 189 2 0 0

Note: Data combines PubMed and Web of Science articles that include “Google Trends” as topic, keyword or in the title. Data as of 21.09.2020.

measuring firm internationalization using google trends

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