Measuring the Happiness, Health, and Stories of Populations

COcoNuTS@networksvox

Happiness

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HappinessSome motivation

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Measuring the Happiness, Health, andStories of Populations

Last updated: 2019/01/14, 22:05:08

Complex Networks | @networksvoxCSYS/MATH 303, Spring, 2019

Prof. Peter Dodds | @peterdodds

Dept. of Mathematics & Statistics | Vermont Complex Systems CenterVermont Advanced Computing Core | University of Vermont

Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.

http://www.uvm.edu

http://www.uvm.edu/pdodds

http://www.uvm.edu/pdodds/teaching/courses/2019-01UVM-303

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http://www.twitter.com/@peterdodds

http://www.uvm.edu/~cems/mathstat/

http://www.uvm.edu/~cems/complexsystems/

http://www.uvm.edu/~vacc/

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These slides are brought to you by:

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These slides are also brought to you by:

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Outline

Introduction

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Key papers:ar

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On the optimal form of branching supply and collection networks

Peter Sheridan Dodds1, ∗

1 Department of Mathematics and Statistics, Center for Complex Systems,

& the Vermont Advanced Computing Center, University of Vermont, Burlington, VT, 05401

(Dated: September 6, 2009)

For the problem of efficiently supplying material to a spatial region from a single source, wepresent a simple scaling argument based on branching network volume minimization that identifieslimits to the scaling of sink density. We discuss implications for two fundamental and unresolvedproblems in organismal biology and geomorphology: how basal metabolism scales with body sizefor homeotherms and the scaling of drainage basin shape on eroding landscapes.

PACS numbers: 89.75.Fb,89.75.Hc,87.19.U-,92.40.Gc

In both natural and man-made systems, branching net-works universally facilitate the essential task of supplyingmaterial from a central source to a widely distributedsink population. Branching networks also underlie thecomplementary process of collecting material from manysources at a single sink. Such networks typically exhib-it structural self-similarity over many orders of magni-tude: river networks drain continents [1, 2, 3], arterialand venal networks move blood between the macroscopicheart and microscopic capillaries [4], and trees and plantsorient leaves in space taking on the roles of both structureand transportation.

We address the following questions regarding supplynetworks: (1) What is the minimum network volumerequired to continually supply material from a source toa population of sinks in some spatial region Ω?; and (2)How does this optimal solution scale if Ω is rescaled allo-metrically? (For convenience, we use the language of dis-tribution, i.e., a single source supplying many sinks.) Ourapproach is inspired by that of Banavar et al. [5, 6] whosought to derive scaling properties of optimal transporta-tion networks in isometrically growing regions based ona flow rate argument; Banavar et al.’s approach followedthe seminal work of West et al. [7] who suggested supplynetworks were key to understanding the metabolic limi-tations of organisms, and focused on network impedanceminimization (see [8, 9]). In contrast to this previ-ous work, our treatment is explicitly geometric. Wealso accommodate four other key features: the ambientdimension, allometrically growing regions, variable sinkdensity, and varying speed of material transportation.

We consider the problem of network supply for a gen-eral class of d-dimensional spatial regions in a D ≥ ddimensional space. Each region Ω has volume V andoverall dimensions L1 × L2 × · · · × Ld (see Fig. 1a). Weallow these length scales to scale as Li ∝ V γi , creatingfamilies of allometrically similar regions. For isometricgrowth, all dimensions scale uniformly meaning γi = 1/d,while for allometric growth, we must have at least one ofthe γi being different (Fig. 1b). For the general caseof allometry, we choose an ordering of γi such that thelength scales are arranged from most dominant to least

b.

L′1L1

Ω(V )L2 L′2

Ω(V ′)a.

2rsink

s

2rsource

c.

FIG. 1: (a) We consider families of d-dimensional spatialregions that scale allometrically with Li ∝ V γi , and exist in aD-dimensional space where D ≥ d. For the d=D=2 exampleshown, γmax = γ1 > γ2, and L1 grows faster than L2. Werequire that each spatial region is star-convex, i.e., from atleast one point all other points are directly observable, andthe single source must be located at any one of these cen-tral points. (b) Distribution (or collection) networks can bethought of as a superposition of virtual vessels. In the exam-ple shown, the source (circle) supplies material to the threesinks (squares). (c) Allowing virtual vessels to expand as theymove away from the source captures a potential decrease inspeed in material flow. For scaling of branching network formto be affected, the radius r of a virtual vessel must scale withvessel length s (measured from the sink) as s−ε.

dominant: γmax = γ1 ≥ . . . ≥ γd.

We assume that isolated sinks are located throughouta contiguous spatial region Ω (volume V ) which containsa single source located at "x = "0. We allow sink den-sity to follow ρ ∼ ρ0(V )(1 + a||"x||)−ζ where a is fixed,ζ ≥ 0, and ||"x|| is the distance from the source. Whenthe exponent ζ = 0, ρ is constant throughout the region(as for capillaries in organisms), but remains a functionof the region’s overall volume V . While decreasing sink

Typeset by REVTEX

“Measuring the happiness of large-scale writtenexpression: Songs, blogs, and presidents.”, Doddsand Danforth, Journal of Happiness Studies, 11,441–456, 2009. [9]

Temporal Patterns of Happiness and Information in aGlobal Social Network: Hedonometrics and Twitter

Peter Sheridan Dodds1,2,3*, Kameron Decker Harris1, Isabel M. Kloumann1,4, Catherine A. Bliss1,

Christopher M. Danforth1,2,3*

1Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont, United States of America, 2Center for Complex Systems, University of Vermont,

Burlington, Vermont, United States of America, 3 Vermont Advanced Computing Center, University of Vermont, Burlington, Vermont, United States of America,

4Department of Physics, University of Vermont, Burlington, Vermont, United States of America

Abstract

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often beenindirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domesticproduct. Here, we examine expressions made on the online, global microblog and social networking service Twitter,uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours toyears. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month spanby over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtainhappiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing wordsets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust andtunable metric can be constructed and defended.

Citation: Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011) Temporal Patterns of Happiness and Information in a Global Social Network:Hedonometrics and Twitter. PLoS ONE 6(12): e26752. doi:10.1371/journal.pone.0026752

Editor: Johan Bollen, Indiana University – Bloomington, United States of America

Received January 15, 2011; Accepted October 3, 2011; Published December 7, 2011

Copyright: ! 2011 Dodds et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors are grateful for the computational resources provided by the Vermont Advanced Computing Center which is supported by NASA (NNX08A096G). KDH was supported by VT-NASA EPSCoR. PSD was supported by NSF CAREER Award # 0846668. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected] (PSD); [email protected] (CMD)

Introduction

One of the great modern scientific challenges we face lies in

understanding macroscale sociotechnical phenomena–i.e., the

behavior of decentralized, networked systems inextricably involv-

ing people, information, and machine algorithms–such as global

economic crashes and the spreading of ideas and beliefs [1].

Accurate description through quantitative measurement is essen-

tial to the advancement of any scientific field, and the shift from

being data scarce to data rich has revolutionized many areas [2–5]

ranging from astronomy [6–8] to ecology and biology [9] to

particle physics [10]. For the social sciences, the now widespread

usage of the Internet has led to a collective, open recording of an

enormous number of transactions, interactions, and expressions,

marking a clear transition in our ability to quantitatively

characterize, and thereby potentially understand, previously

hidden as well as novel microscale mechanisms underlying

sociotechnical systems [11].

While there are undoubtedly limits to that which may

eventually be quantified regarding human behavior, recent studies

have demonstrated a number of successful and diverse method-

ologies, all impossible (if imaginable) prior to the Internet age.

Three examples relevant to public health, markets, entertainment,

history, evolution of language and culture, and prediction are (1)

Google’s digitization of over 15 million books and an initial

analysis of the last two hundred years, showing language usage

changes, censorship, dynamics of fame, and time compression of

collective memory [12,13]; (2) Google’s Flu Trends [14–16] which

allows for real-time monitoring of flu outbreaks through the proxy

of user search; and (3) the accurate prediction of box office success

based on the rate of online mentions of individual movies [17] (see

also [18]).

Out of the many possibilities in the ‘Big Data’ age of social

sciences, we focus here on measuring, describing, and under-

standing the well-being of large populations. A measure of ‘societal

happiness’ is a crucial adjunct to traditional economic measures

such as gross domestic product and is of fundamental scientific

interest in its own right [19–22].

Our overall objective is to use web-scale text analysis to

remotely sense societal-scale levels of happiness using the singular

source of the microblog and social networking service Twitter.

Our contributions are both methodological and observational.

First, our method for measuring the happiness of a given text,

which we introduced in [23] and which we improve upon greatly

in the present work, entails word frequency distributions combined

with independently assessed numerical estimates of the ‘happiness’

of over 10,000 words obtained using Amazon’s Mechanical Turk

[24]. We describe our method in full below and demonstrate its

robustness. We refer to our data set as ‘language assessment by

Mechanical Turk 1.0’, which abbreviates as labMT 1.0, and we

provide all data as Data Set S1.

Second, using Twitter as a data source, we are able to explore

happiness as a function of time, space, demographics, and network

structure, with time being our focus here. Twitter is extremely

PLoS ONE | www.plosone.org 1 December 2011 | Volume 6 | Issue 12 | e26752

“Temporal patterns of happiness andinformation in a global social network:Hedonometrics and Twitter”Dodds et al.,PLoS ONE, 6, e26752, 2011. [11]

Positivity of the English Language

Isabel M. Kloumann1,4, Christopher M. Danforth1,2,3*, Kameron Decker Harris1, Catherine A. Bliss1, Peter

Sheridan Dodds1,2,3*

1Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont, United States of America, 2Center for Complex Systems, University of Vermont,

Burlington, Vermont, United States of America, 3 Vermont Advanced Computing Center, University of Vermont, Burlington, Vermont, United States of America,

4Department of Physics, University of Vermont, Burlington, Vermont, United States of America

Abstract

Over the last million years, human language has emerged and evolved as a fundamental instrument of socialcommunication and semiotic representation. People use language in part to convey emotional information, leading to thecentral and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languagesneutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the mostfrequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of wordpositivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency ofword use.

Citation: Kloumann IM, Danforth CM, Harris KD, Bliss CA, Dodds PS (2012) Positivity of the English Language. PLoS ONE 7(1): e29484. doi:10.1371/journal.pone.0029484

Editor: Luıs A. Nunes Amaral, Northwestern University, United States of America

Received October 26, 2011; Accepted November 29, 2011; Published January 11, 2012

Copyright: ! 2012 Kloumann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors are grateful for the computational resources provided by the Vermont Advanced Computing Center which is supported by NASA (NNX08A096G). KDH was supported by VT-NASA EPSCoR. PSD was supported by NSF CAREER Award # 0846668. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected] (PSD); [email protected] (CMD)

Introduction

While we regard ourselves as social animals, we have a history of

actions running from selfless benevolence to extreme violence at all

scales of society, and we remain scientifically and philosophically

unsure as to what degree any individual or group is or should be

cooperative and pro-social. Traditional economic theory of human

behavior, for example, assumes that people are inherently and

rationally selfish–a core attribute of homo economicus–with the

emergence of global cooperation thus rendered a profound mystery

[1,2]. Yet everyday experience and many findings of psychology,

behavioral economics, and neuroscience indicate people favour

seemingly irrational heuristics [3,4] over strict rationality as

exemplified in loss-aversion [5], confirmation bias [6], and altruistic

punishment [7]. Religions and philosophies similarly run the gamut

in prescribing the right way for individuals to behave, from the

universal non-harming advocated by Jainism, Gandhi’s call for non-

violent collective resistance, and exhortations toward altruistic

behavior in all major religions, to arguments for the necessity of a

Monarch [8], the strongest forms of libertarianism, and the

‘‘rational self-interest’’ of Ayn Rand’s Objectivism [9].

In taking the view that humans are in part story-tellers–homo

narrativus–we can look to language itself for quantifiable evidence of

our social nature. How is the structure of the emotional content

rendered in our stories, fact or fiction, and social interactions

reflected in the collective, evolutionary construction of human

language? Previous findings are mixed: suggestive evidence of a

positive bias has been found in small samples of English words

[10–12], framed as the Pollyanna Hypothesis [10] and Linguistic

Positivity Bias [12], while experimental elicitation of emotional

words has instead found a strong negative bias [13].

To test the overall positivity of the English language, and in

contrast to previous work [11,13,14], we chose words based solely

on frequency of use, the simplest and most impartial gauge of word

importance. We focused on measuring happiness, or psychological

valence [15], as it represents the dominant emotional response

[16,17]. With this approach, we examined four large-scale text

corpora (see Tab. 1 for details): Twitter, The Google Books Project

(English), The New York Times, and Music lyrics. These corpora,

which we will refer to as TW, GB, NYT, and ML, cover a wide

range of written expression including broadcast media, opinion,

literature, songs, and public social interactions ([18]), and span the

gamut in terms of grammatical and orthographic correctness.

We took the top 5000 most frequently used words from each

corpus, and merged them to form a resultant list of 10,222 unique

words. We then used Amazon’s Mechanical Turk [19,20] to

obtain 50 independent evaluations per word on a 1 to 9 integer

scale, asking participants to rate their happiness in response to

each word in isolation (1 = least happy, 5= neutral, and 9=most

happy [14,21]). While still evolving, Mechanical Turk has proved

over the last few years to be a reliable and fast service for carrying

out large-scale social science research [22–26].

We computed the average happiness score and standard

deviation for each word. We obtained sensible results that showed

excellent statistical agreement with previous studies for smaller

word sets, including a translated Spanish version (see [14,20,

27] for details). The highest and lowest scores were

havg(‘laughter’)=8.50 and havg(‘terrorist’)=1.30, with expectedly

neutral words averaging near 5, e.g., havg(‘the’)=4.98 and

havg(‘it’)=5.02. We refer to our ongoing studies as Language

Assessment by Mechanical Turk, using the abbreviation labMT

1.0 data set for the present work (the full data set is provided as

PLoS ONE | www.plosone.org 1 January 2012 | Volume 7 | Issue 1 | e29484

“Positivity of the English language”, Kloumann etal., PLoS ONE, 7, e29484, 2012. [23]

Reagan et al. EPJ Data Science ( 2017) 6:28

DOI 10.1140/epjds/s13688-017-0121-9

REGULAR ART ICLE Open Access

Sentiment analysis methods forunderstanding large-scale texts: a case forusing continuum-scored words and wordshift graphsAndrew J Reagan1*, Christopher M Danforth1, Brian Tivnan2, Jake Ryland Williams3 and

Peter Sheridan Dodds1

*Correspondence:

[email protected] of Vermont, 85 South

Prospect St, Burlington, VT 05405,

USA

Full list of author information is

available at the end of the article

Abstract

The emergence and global adoption of social media has rendered possible the

real-time estimation of population-scale sentiment, an extraordinary capacity which

has profound implications for our understanding of human behavior. Given the

growing assortment of sentiment-measuring instruments, it is imperative to

understand which aspects of sentiment dictionaries contribute to both their

classification accuracy and their ability to provide richer understanding of texts. Here,

we perform detailed, quantitative tests and qualitative assessments of 6

dictionary-based methods applied to 4 different corpora, and briefly examine a

further 20 methods. We show that while inappropriate for sentences,

dictionary-based methods are generally robust in their classification accuracy for

longer texts. Most importantly they can aid understanding of texts with reliable and

meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of

a given text’s lexicon when weighted by word usage frequency; and (2) words are

scored on a continuous scale.

Keywords: sentiment; sentiment analysis; sentiment dictionaries; language; natural

language processing; data visualization; text visualization

1 Introduction

As we move further into what might be called the Sociotechnocene — with increasingly

more interactions, decisions, and impact being made by globally distributed people and

algorithms — the myriad human social dynamics that have shaped our history have be-

come far more visible and measurable than ever before. Of the many ways we are now

able to characterize social systems in microscopic detail, sentiment detection for pop-

ulations at all scales has become a prominent research arena. Attempts to leverage on-

line expression for sentiment mining include prediction of stock markets [–], assessing

responses to advertising, real-time monitoring of global happiness [], and measuring a

health-related quality of life []. The diverse set of instruments produced by this work

now provide indicators that help scientists understand collective behavior, inform pub-

lic policy makers, and, in industry, gauge the sentiment of public response to marketing

© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in anymedium, pro-vided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.

“Sentiment analysis methods for understandinglarge-scale texts: A case for using continuum-scoredwords and word shift graphs”, Reagan et al., EPJData Science, 6, , 2017. [31]

http://www.uvm.edu


http://www.uvm.edu/pdodds/research/papers/others/everything/dodds2009c.pdf



http://www.uvm.edu/pdodds/research/papers/others/everything/dodds2011e.pdf




http://www.uvm.edu/pdodds/research/papers/others/everything/kloumann2012a.pdf

http://www.uvm.edu/pdodds/research/papers/others/everything/kloumann2012a.pdf

http://www.uvm.edu/pdodds/research/papers/others/everything/reagan2017a.pdf





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Economics, Schmeconomics

Alan Greenspan (September 18, 2007):

“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...

If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,

I don’t need any of this other stuff.

I could forecast the economy better than anyway I know.”

http://wikipedia.org

http://www.uvm.edu




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Analysis

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Alan Greenspan (September 18, 2007):

“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...

If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,

I don’t need any of this other stuff.

I could forecast the economy better than anyway I know.” http://wikipedia.org

http://www.uvm.edu




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Greenspan continues:

“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:

“You just bummed the @*!# out of me.”

wildbluffmedia.com

From the Daily Show (September 18, 2007; @5:13)

The full inteview is here.

http://www.uvm.edu


wildbluffmedia.com

http://www.thedailyshow.com

http://www.thedailyshow.com/video/index.jhtml?videoId=102970&title=alan-greenspan


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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years.

I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com




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Hedonometer

Analysis

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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,

and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com




Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

SOTU

Geography

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Other Emotions

References

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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is.

Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com




Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

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Geography

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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today.

And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com




Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

SOTU

Geography

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References

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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged.

We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com




Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

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Geography

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Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”

Jon Stewart:


wildbluffmedia.com



http://www.uvm.edu


wildbluffmedia.com



This is a Collateralized Debt Obligation:


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Robert Kennedy on the Gross Domestic Product(GDP) in 1968:“It measures everything except that which makes lifeworthwhile.

And it can tell us everything aboutAmerica except why we are proud that we areAmericans.” [21, 20]

http://www.uvm.edu



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Robert Kennedy on the Gross Domestic Product(GDP) in 1968:“It measures everything except that which makes lifeworthwhile. And it can tell us everything aboutAmerica except why we are proud that we areAmericans.” [21, 20]

http://www.uvm.edu



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Basic Science ≃ Describe + Explain:

Lord Kelvin (possibly):

“To measure is to know.”

“If you cannot measure it, youcannot improve it.”

But also:

“There is nothing new to bediscovered in physics now. Allthat remains is more and moreprecise measurement.”

“X-rays will prove to be a hoax.”

http://www.uvm.edu



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Hedonometer

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Basic Science ≃ Describe + Explain:

Lord Kelvin (possibly):

“To measure is to know.”

“If you cannot measure it, youcannot improve it.”

But also: “There is nothing new to be

discovered in physics now. Allthat remains is more and moreprecise measurement.”

“X-rays will prove to be a hoax.”

http://www.uvm.edu



Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

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Geography

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A brief history of measuring time: Megaliths for Big Time Sundials, 1500 BC, Egypt (solid for over 2000 years) Escapements (200s), Hourglasses (1300s?),

Pendulum clocks (Galileo, 1500s) Chronometers, 1700s:

“Longitude: The True Story of a Lone GeniusWho Solved the Greatest Scientific Problemof His Time , publisher=BloomsburyPublishing, US”by Dava Sobel (2007). [33]

Billionths of a second accuracy: Atomic clocks(Lord Kelvin, 1879)

http://www.uvm.edu


http://www.amazon.com/dp/080271529X/





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Measuring temperature was thought impossible:

From “Bias in Mental Testing”, Arthur Jensen, 1980 [17]

per @SilverVVulpes: Also: Inventing Temperature, HasokChang, 2004 [3]

http://www.uvm.edu


https://twitter.com/SilverVVulpes/status/850439061273804801


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Measuring temperature was thought impossible:

From “Bias in Mental Testing”, Arthur Jensen, 1980 [17]

per @SilverVVulpes: Also: Inventing Temperature, HasokChang, 2004 [3]

http://www.uvm.edu


https://twitter.com/SilverVVulpes/status/850439061273804801


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http://www.uvm.edu



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What matters and what’s measurable:

http://www.uvm.edu



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Outline

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References

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Panometer—Three kinds of lexical meters:

1. Principled lexical meters: The Hedonometer.

The Lexicocalorimeter.

2. Ground truth lexical meters: Insomniometer. Hangoverometer.

3. Bootstrap lexical meters: Boredometer. Hashtagometers.

http://www.uvm.edu



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Panometer—Three kinds of lexical meters:

1. Principled lexical meters: The Hedonometer. The Lexicocalorimeter.

2. Ground truth lexical meters: Insomniometer. Hangoverometer.

3. Bootstrap lexical meters: Boredometer. Hashtagometers.

http://www.uvm.edu



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Measuring Happiness:

Socrates et al.:eudaimonia [18]

Bentham:hedonisticcalculus

Jefferson:…the pursuit ofhappiness

http://www.uvm.edu



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Early drafts:

http://www.uvm.edu



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Happiness:

Even the odd modern economistis happy:

“Happiness” by RichardLayard [24]

[amazon]

http://www.uvm.edu


http://www.amazon.com/Happiness-Lessons-Science-Richard-Layard/dp/1594200394


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What makes us happy?—Layard’ssummary:Dominant factors:

Familyrelationships

Financialsituation

Work Community and

Friends

Health Personal Values Personal

Freedom

Unimportant factors:

Age Gender Education

Inherentintelligence

Looks

http://www.uvm.edu



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Desiring happiness—not just for boffins: Average people routinely report being happy is

what they want most in life [24, 25, 8]

And it matters: “Happy people live longer:…”Survey by Diener and Chan. [8]

http://www.uvm.edu



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Desiring happiness—not just for boffins: Average people routinely report being happy is

what they want most in life [24, 25, 8]

And it matters: “Happy people live longer:…”Survey by Diener and Chan. [8]

National indices ofwell-being: Bhutan UK France Australia

http://www.uvm.edu



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Outline

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References

http://www.uvm.edu



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Some easy knocks:

“The Passionate State of Mind: And OtherAphorisms”by Eric Hoffer (1954). [16]

“The search for happiness is one of the chief sourcesof unhappiness.”

http://www.uvm.edu


http://www.amazon.com/dp/1933435097/




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Some easy knocks:

Colbert: “Happiness is totally overrated …”

“Happiness is for the weak.”

Full interview with Jennifer Senior here (2014/02/03)

http://www.uvm.edu


http://www.cc.com/video-clips/nocpjv/the-colbert-report-jennifer-senior


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Some easy knocks:

Colbert: “Happiness is totally overrated …”“Happiness is for the weak.”

Full interview with Jennifer Senior here (2014/02/03)

http://www.uvm.edu


http://www.cc.com/video-clips/nocpjv/the-colbert-report-jennifer-senior


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30 Rock, S7E8:

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy.

‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’

You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto?

A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser.

Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy.

You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”

LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”

JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service.

One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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30 Rock, S7E8:

JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.

http://www.uvm.edu



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Meaning rather than happiness:

“Mindfulness in Plain English”by Henepola Gunaratana (1992). [15]

“Flow”by Mihaly Csikszentmihalyi (1990). [6]

Can we measure Flow in a big data way? Maybe drops in social media usage indicate

people are doing okay?

http://www.uvm.edu







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Emotional content

So how does one measure1. happiness?2. levels of other emotional states?

Just ask people how happy they are.

Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)

But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]

costly

http://www.uvm.edu



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Emotional content

So how does one measure1. happiness?2. levels of other emotional states?

Just ask people how happy they are. Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)

But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]

costly

http://www.uvm.edu



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Happiness, attention, and doing:

Fig. 1. Mean happiness reported during each ac-tivity (top) and while mind wandering to unpleas-ant topics, neutral topics, pleasant topics or notmind wandering (bottom). Dashed line indicatesmean of happiness across all samples. Bubble areaindicates the frequency of occurrence. The largestbubble (“not mind wandering”) corresponds to53.1% of the samples, and the smallest bubble(“praying/worshipping/meditating”) corresponds to0.1% of the samples.

Killingsworth andGilbert, Science,2010 [22]

http://www.uvm.edu



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Outline

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http://www.uvm.edu



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We’d like to build an ‘hedonometer’:

An instrument to ‘remotely-sense’emotional states and levels, inreal time or post hoc.

Ideally:

Transparent Fast Based on written

expression Uses human

evaluation

Non-reactive Complementary to

self-reportedmeasures

Improvable

http://www.uvm.edu



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We don’t want to end up here:

Science Policy

http://www.uvm.edu



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Or here: Facebook Lexicon SentimentAnalysis (2008)

http://www.uvm.edu



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Facebook Lexicon Sentiment Analysis

Binary decision on emotional content

Limitation: Sentiments are classified as eitherpositive or negative.

‘I like Sarah Palin’ given same score as ‘SarahPalin’s voice fills me with unbridled joy!’

http://www.uvm.edu



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Measuring Emotional Content Idea: Build on measures of the emotional content

of individual words.

Osgood et al. (1957) [30] identifieda basis of three psychological variables assemantic differentials:

Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.

http://www.uvm.edu



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of individual words. Osgood et al. (1957) [30] identified

a basis of three psychological variables assemantic differentials:

Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.

http://www.uvm.edu



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of individual words. Osgood et al. (1957) [30] identified

a basis of three psychological variables assemantic differentials: Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.

http://www.uvm.edu



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ANEW study

ANEW = “Affective Norms for English Words”

Study: participants shown lists of isolated words Asked to grade each word’s valence, arousal, and

dominance level Integer scale of 1–9

𝑁 =1034 words—previously identified as bearingemotional weight

Participants = College students (*cough*) Results published by Bradley and Lang (1999) [2]

http://www.uvm.edu



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1999 ANEW study—three 1–9 scales: [2]valence:

arousal:

dominance:

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ANEW study:

Valence = Happiness: Valence scale presented to participants as a

‘happy-unhappy scale.’

Participants were further told:

“At one extreme of this scale, you are happy,pleased, satisfied, contented, hopeful. …

The other end of the scale is when you feelcompletely unhappy, annoyed, unsatisfied,melancholic, despaired, or bored.”

http://www.uvm.edu



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ANEW study:

Valence = Happiness: Valence scale presented to participants as a

‘happy-unhappy scale.’ Participants were further told:

“At one extreme of this scale, you are happy,pleased, satisfied, contented, hopeful. …

The other end of the scale is when you feelcompletely unhappy, annoyed, unsatisfied,melancholic, despaired, or bored.”

http://www.uvm.edu



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ANEW study words—examples

0 50 1001502001

2

3

4

5

6

7

8

9

funeral/rape/suicide

trauma/hostage/disgusted

fault/corrupt/lawsuit

derelict/neurotic/vanity

engine/paper/street

optimism/pancakes/church

glory/luxury/trophy

love/paradise/triumphant

frequency

vale

nce v

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Analysing text: Simplest measure for a text:𝜃avg = 𝑁∑𝑖=1 𝑝𝑖𝜃𝑖

where 𝑝𝑖 is fractional abundance of word 𝑖 and 𝜃 isaverage valence, arousal, or dominance for word 𝑖.

Focus on happiness (valence), 𝜃 = ℎ. Average happiness typically falls between 5 and 7.

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Focus on happiness (valence), 𝜃 = ℎ.

Average happiness typically falls between 5 and 7.

http://www.uvm.edu



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Focus on happiness (valence), 𝜃 = ℎ. Average happiness typically falls between 5 and 7.

http://www.uvm.edu


Measuring the perceived happiness of atext:

ANEWwords

11. perfume

14. lie

k=1. love2. mother3. baby4. beauty5. truth6. people7. strong8. young9. girl10. movie

12. queen13. name

8.728.398.227.827.807.337.116.896.876.866.766.445.552.79

11311

12411111

from a movie scene.

’cause the lie becomes the truth.

And be careful of what you do

She’s just a girl who claims

Billie Jean is not my lover,

that I am the one.

Michael Jackson’s Billie Jean

vMichaelJackson

vThriller

= 7.1

= 6.4

= 6.3

=vtext ∑

kfk

vBillie Jean

∑

kvkfk

fk

“She was more like a beauty queen

2And mother always told me,

be careful who you love.

vkLyrics for

Temperature-like measure—large numbers only.

Not meant to be used at level of sentence,paragraph, song, tweet, ...

Important: Social measure of sentiment. Later: see instrument is tunable.


ANEWwords

11. perfume

14. lie


12. queen13. name

8.728.398.227.827.807.337.116.896.876.866.766.445.552.79

11311

12411111

from a movie scene.





that I am the one.


vMichaelJackson

vThriller

= 7.1

= 6.4

= 6.3

=vtext ∑

kfk

vBillie Jean

∑

kvkfk

fk




vkLyrics for

Temperature-like measure—large numbers only. Not meant to be used at level of sentence,

paragraph, song, tweet, ...

Important: Social measure of sentiment. Later: see instrument is tunable.


ANEWwords

11. perfume

14. lie


12. queen13. name

8.728.398.227.827.807.337.116.896.876.866.766.445.552.79

11311

12411111

from a movie scene.





that I am the one.


vMichaelJackson

vThriller

= 7.1

= 6.4

= 6.3

=vtext ∑

kfk

vBillie Jean

∑

kvkfk

fk




vkLyrics for


paragraph, song, tweet, ... Important: Social measure of sentiment.

Later: see instrument is tunable.


ANEWwords

11. perfume

14. lie


12. queen13. name

8.728.398.227.827.807.337.116.896.876.866.766.445.552.79

11311

12411111

from a movie scene.





that I am the one.


vMichaelJackson

vThriller

= 7.1

= 6.4

= 6.3

=vtext ∑

kfk

vBillie Jean

∑

kvkfk

fk




vkLyrics for


paragraph, song, tweet, ... Important: Social measure of sentiment. Later: see instrument is tunable.


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Outline

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References

http://www.uvm.edu


Daft Punk’s “Around the World”:

Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world











Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world.







Magic: Low entropy, high energy.

https://en.wikipedia.org/wiki/Around_the_World_(Daft_Punk_song)


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Outline

Introduction

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References

http://www.uvm.edu



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Song Lyrics—average happiness

1960 1970 1980 1990 2000 20105.9

6

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

year

mea

n va

lenc

e v av

g

http://www.uvm.edu



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Song Lyrics—measurement robustness

19601965197019751980198519901995200020052010−0.4

−0.2

0

0.2

0.4

0.6

0.8

rel.

val.

(v −

v avg)

year

5.8 6 6.2 6.4 6.60

100

200

vavg

coun

t

100 random subsets of 750 ANEW words

http://www.uvm.edu



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Song Lyrics—average happiness of genres:

1960 1970 1980 1990 2000 20104.5

5

5.5

6

6.5

7

year

mea

n va

lenc

e v av

g

Gospel/Soul (6.91)Pop (6.69)Reggae (6.40)Rock (6.27)Rap/Hip−Hop (6.01)Punk (5.61)Metal/Industrial (5.10)

http://www.uvm.edu


Happiness Word Shift Graph (early version):

−20 −10 0 10

25242322212019181716151413121110987654321 love ↓

lonely ↓hate ↑pain ↑baby ↓death ↑dead ↑home ↓sick ↑fear ↑hit ↑hell ↑fall ↑sin ↑lost ↑

sad ↓burn ↑lie ↑scared ↑afraid ↑music ↓

life ↑god ↑

trouble ↓loneliness ↓

Per word valence shift ∆i

Wor

d nu

mbe

r i

Per word drop in valence of lyrics from 1980−2007 relative to valence of lyrics from 1960−1979:

lonely ↓sad ↓

trouble ↓loneliness ↓

devil ↓Decreases in relativelylow valence wordscontribute to increasein average valence

life ↑god ↑

truth ↑party ↑

sex ↑Increases in relativelyhigh valence wordscontribute to increasein average valence

hate ↑pain ↑death ↑dead ↑sick ↑

Increases in relativelylow valence wordscontribute to dropin average valence

love ↓baby ↓home ↓music ↓good ↓

Decreases in relativelyhigh valence wordscontribute to dropin average valence

Key:

Word shifts are word clouds for grown ups.

Word data shift details:

Given two texts 𝑇ref and 𝑇comp:

Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)

avg

Evident question: Which words contribute themost to this change?

Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)

avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)

avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|




avg




avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓

Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|




avg




avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100

Rank words by |𝛿ℎavg,i|




avg




avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|


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ℎ(comp)avg − ℎ(ref)

avg = 𝑁∑𝑖=1 ℎavg(𝑤𝑖)𝑝(comp)𝑖 − 𝑁∑𝑖=1 ℎavg(𝑤𝑖)𝑝(ref)𝑖= 𝑁∑𝑖=1 ℎavg(𝑤𝑖) [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]

= 𝑁∑𝑖=1 [ℎavg(𝑤𝑖) − ℎ(ref)avg ] [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]

where𝑁∑𝑖=1 ℎ(ref)avg [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ] = ℎ(ref)

avg

𝑁∑𝑖=1 [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]= ℎ(ref)

avg (1 − 1) = 0.

http://www.uvm.edu



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+↑: Increased usage of relatively positive words—If a wordis happier than text 𝑇ref (+) and appears relativelymore often in text 𝑇comp (↑), then the contribution tothe difference ℎ(comp)

avg − ℎ(ref)avg is positive;−↓: Decreased usage of relatively negative words—If a

word is less happy than text 𝑇ref (−) and appearsrelatively less often in text 𝑇comp (↓), then thecontribution to the difference ℎ(comp)

avg − ℎ(ref)avg is also

positive;+↓: Decreased usage of relatively positive words—If a wordis happier than text 𝑇ref (+) and appears relatively lessoften in text 𝑇comp (↓), then the contribution to thedifference ℎ(comp)

avg − ℎ(ref)avg is negative; and−↑: Increased usage of relatively negative words—If a word

is less happy than text 𝑇ref (−) and appears relativelymore often in text 𝑇comp (↑), then the contribution tothe difference ℎ(comp)

avg − ℎ(ref)avg is also negative.

http://www.uvm.edu



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Top 50 of ≃ 20,000 artists:Rank Artist ℎavg1 All-4-One 7.152 Luther Vandross 7.123 S Club 7 7.054 K Ci & JoJo 7.045 Perry Como 7.046 Diana Ross & The Supremes 7.037 Buddy Holly 7.028 Faith Evans 7.019 The Beach Boys 7.0110 Jon B 6.9811 Dru Hill 6.9612 Earth Wind & Fire 6.9513 Ashanti 6.9514 Otis Redding 6.9315 Faith Hill 6.9316 NSync 6.9317 The Supremes 6.9118 The Partridge Family 6.9119 Kelly Price 6.8920 Tamia 6.8921 Avant 6.8822 Jennifer Lopez 6.8823 Vanessa Williams 6.8724 Babyface 6.8725 E Rotic 6.87

Rank Artist ℎavg26 Sarah Connor 6.8627 Darlene Zschech 6.8628 Mary J Blige 6.8629 Steve Miller Band 6.8630 New Edition 6.8631 Mandy Moore 6.8632 Alicia Keys 6.8533 Cher 6.8534 Modern Talking 6.8535 Mario 6.8436 Aretha Franklin 6.8437 Jessica Simpson 6.8438 112 6.8439 Backstreet Boys 6.8340 Billy Gilman 6.8341 B2K 6.8242 Stevie Wonder 6.8243 John Legend 6.8144 Ricky Nelson 6.7945 Lionel Richie 6.7946 98 Degrees 6.7947 Boyzone 6.7948 Gerald Levert 6.7949 Nat King Cole 6.7850 Marques Houston 6.78

(criteria: ≥ 50 songs and ≥ 1000 ANEW words)

http://www.uvm.edu



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Bottom 50 of ≃ 20,000 artists:Rank Artist ℎavg1 Slayer 4.802 Misfits 4.883 Staind 4.934 Slipknot 4.985 Darkthrone 4.986 Death 5.027 Black Label Society 5.058 Pig 5.089 Voivod 5.1410 Fear Factory 5.1511 Iced Earth 5.1612 Simple Plan 5.1613 Machine Head 5.1714 Metallica 5.1915 Dimmu Borgir 5.2016 Mudvayne 5.2117 Linkin Park 5.2218 Papa Roach 5.2219 Audioslave 5.2420 Rage Against The Machine 5.2421 Cradle Of Filth 5.2522 Dark Tranquility 5.2623 Jack Off Jill 5.2824 Evanescence 5.3025 Twiztid 5.33

Rank Artist ℎavg26 Nine Inch Nails 5.3427 Sevendust 5.3428 Annihilator 5.3529 Biohazard 5.3630 Insane Clown Posse 5.3631 Megadeth 5.3632 Manowar 5.3733 Zebrahead 5.3834 Danzig 5.3935 Acid Drinkers 5.4036 Dag Nasty 5.4037 Iron Maiden 5.4038 Flotsam And Jetsam 5.4139 Powerman 5000 5.4240 Anthrax 5.4341 Rhapsody 5.4342 Korn 5.4343 Rage 5.4444 Accept 5.4545 Esham 5.4646 Blind Guardian 5.4647 White Zombie 5.4748 Helloween 5.5049 W A S P 5.5050 Green Day 5.50

(criteria: ≥ 50 songs and ≥ 1000 ANEW words)

http://www.uvm.edu


Text: ℎavg Words with a similar score:

Soul/Gospellyrics [10]

6.9 chocolate (6.88), leisurely (6.88), penthouse(6.81)

Pop lyrics [10] 6.7 dream (6.73), honey (6.73), sugar (6.74)Dante’s Paradise [?] 6.5 muffin (6.57), rabbit (6.57), smooth (6.58)Tweets, 9/9/2008 to12/31/2010

6.4 thought (6.39), face (6.39), blond (6.42)

Rock lyrics [10] 6.3 church (6.28), tree (6.32), air (6.34)Enron Emails [?] 6.2 clouds (6.18), alert (6.20), computer (6.24)State of the UnionMessages [10]

6.1 grass (6.12), idol (6.12), bottle (6.15)

New York Times(1987–2007) [32]

6.0 hotel (6.00), tennis (6.02), wonder (6.03)

Blogs [10] 5.8 owl (5.80), whistle (5.81), humble (5.86)Dante’s Inferno [?] 5.5 glacier (5.50), repentant (5.53), mischief (5.57)Heavy Metallyrics [10]

5.4 lamp (5.41), elevator (5.44), truck (5.47)


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Lexicon Valley, Episode #62, June 17, 2015 Mike Vuolo and Bob Garfield.

Language has a Positivity Bias. How did we measure that?

http://www.uvm.edu


http://www.slate.com/articles/podcasts/lexicon_valley/2015/06/lexicon_valley_the_pollyanna_hypothesis_all_languages_have_a_positivity.html


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Outline

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References

http://www.uvm.edu



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Data sets: Blog phrases containing “I feel...”, “I am feeling”,

etc., taken from wefeelfine.org (API, 2005–2010)

Created byJonathan Harris& Sep Kamvar

http://www.uvm.edu


http://www.wefeelfine.org


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Jonathan Harris:

2007 TED Talk: The Web’s secret stories

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https://www.ted.com/talks/jonathan_harris_tells_the_web_s_secret_stories


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So I really consider myself a storyteller. But I don’treally tell stories in the usual way, in the sense that Idon’t usually tell my own stories. Instead, I’m reallyinterested in building tools that allow large numbers ofother people to tell their stories, people all around theworld. I do this because I think that people actuallyhave a lot in common. I think people are very similar,but I also think that we have trouble seeing that.

http://www.uvm.edu



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wefeelfine.org:

http://www.uvm.edu



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wefeelfine.org:

http://www.uvm.edu



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wefeelfine.org:

http://www.uvm.edu


Blogs—Overall trend

A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A5.7

5.8

5.9

6

6.1

6.2

6.3

6.4

9/119/10

9/109/10

US Election 11/4

US Inauguration 1/20

MichaelJackson

♥

♥

♥ ♥♥

2005 2006 2007 2008 2009 2010

aver

age

happ

ines

s h avg


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From wefeelfine.org by Jonathan Harris & SepKamvar

13 20 30 40 50 60 70 805.5

5.6

5.7

5.8

5.9

6

6.1

blogger age

vale

nce

(v)

Average happiness as a function of the agebloggers report they will turn in the year of theirposting.

http://www.uvm.edu


http://www.wefeelfine.org


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Blogs—Age

Self-report studies find little variation in happinesswith age [12, 13]

Surprising: Expect a rise and fall. A ‘challenge’ for theory... Related to the Easterlin Paradox:

Money doesn’t buy happiness But maybe it does a little bit—Veenhoven &

Hagerty (2003) and Wolfers & Stevenson (2008).

http://www.uvm.edu



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Blogs—Age


Surprising: Expect a rise and fall.

A ‘challenge’ for theory... Related to the Easterlin Paradox:



http://www.uvm.edu



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Blogs—Age


Surprising: Expect a rise and fall. A ‘challenge’ for theory...

Related to the Easterlin Paradox:Money doesn’t buy happiness

But maybe it does a little bit—Veenhoven &Hagerty (2003) and Wolfers & Stevenson (2008).

http://www.uvm.edu



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Blogs—Age



Money doesn’t buy happiness

But maybe it does a little bit—Veenhoven &Hagerty (2003) and Wolfers & Stevenson (2008).

http://www.uvm.edu



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Blogs—Age





http://www.uvm.edu



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−20 0 20

1

5

10

15

20

25

−↑sick

−↑hate

−↑stupid

−↑sad

happy +↑

love +↑

−↑depressed

−↑bored

−↑lonely

−↑alone

−↑mad

−↑pain

+↓life

loved +↑

−↑upset

−↑fat

fun +↑

−↑dead

−↑scared

−↑terrible

friend +↑

people +↑

−↑confused

time −↓

−↑hurt

Per word average happiness shift δhavg,r (%)

Word

rank

r

Tre f: born in 1960-1969 (havg=5.96)Tcom p: 14 year s old (havg=5.55)

Text s iz e :T re f T c omp

+↓ +↑

−↑ −↓

Balan ce :

−169 : +69

−100 0

100

101

102

103

∑r

i =1δ havg, i

http://www.uvm.edu



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Blogs—Latitude

0 10 20 30 40 50 60 70

5.7

5.75

5.8

5.85

latitude |degrees|

Near equator—social factors Increase in ‘sad’, ‘bored’,

‘lonely’, ‘stupid’, ‘guilty’ Decrease in ‘good’ and

‘people’

Near poles—social/psychological/climate Increase in ‘sick’, ‘guilty’,

‘cold’, ‘depressed’, and‘headache’ and decrease of‘love’ and ‘life.’

Offset by decrease in ‘hurt’and ‘pain.’

More ‘bed’ and ‘sleep.’

http://www.uvm.edu



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−100 −50 0 50 100

1

5

10

15

20

25

love +↑

−↑hurt

−↑hate

−↑sad

+↓good

−↑alone

baby +↑

loved +↑

happy +↑

−↑stupid

−↑guilty

−↑sick

heart +↑

−↑scared

−↑lost

+↓music

+↓free

death −↓

life +↑

family +↑

+↓christmas

cold −↓

−↑upset

friend +↑

dead −↓


Word

rank

r

Tre f: Male (havg=5.91)Tcom p: Female (havg=5.89)


+↓ +↑

−↑ −↓

Balan ce :

−607 : +507

−100 0

100

101

102

103

∑r

i =1δ havg, i

http://www.uvm.edu



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.70 of 155

Outline

Introduction

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References

http://www.uvm.edu



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Presidential happiness:

1910 1930 1950 1970 1990 20105.5

5.6

5.7

5.8

5.9

6

6.1

6.2

6.3

6.4

6.5

The

odor

e R

oose

velt

Will

iam

How

ard

Taf

t

Woo

drow

Wils

on

War

ren

G. H

ardi

ng Cal

vin

Coo

lidge

Her

bert

Hoo

ver

Fra

nklin

D. R

oose

velt

Har

ry S

. Tru

man

Dw

ight

D. E

isen

how

er

John

F. K

enne

dy

Lynd

on B

. Joh

nson

Ric

hard

Nix

on

Ger

ald

R. F

ord

Jim

my

Car

ter

Ron

ald

Rea

gan

Geo

rge

Bus

h

Will

iam

J. C

linto

n

Geo

rge

W. B

ush

year

v avg

http://www.uvm.edu


Presidential happiness:

1790 1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 20105.55.65.75.85.9

66.16.26.36.46.5

Geo

rge

Was

hing

ton

John

Ada

ms

Tho

mas

Jef

fers

on Jam

es M

adis

on

Jam

es M

onro

e

John

Qui

ncy

Ada

ms

And

rew

Jac

kson

Mar

tin v

an B

uren

John

Tyl

erJa

mes

K. P

olk

Zac

hary

Tay

lor

Mill

ard

Fill

mor

eF

rank

lin P

ierc

eJa

mes

Buc

hana

nA

brah

am L

inco

lnA

ndre

w J

ohns

on

Uly

sses

S. G

rant

Rut

herf

ord

B. H

ayes

Che

ster

A. A

rthu

r Ben

jam

inH

arris

on

Gro

ver

Cle

vela

nd

Will

iam

McK

inle

y

The

odor

e R

oose

velt

Will

iam

How

ard

Taf

t

Woo

drow

Wils

onW

arre

n G

. Har

ding

Cal

vin

Coo

lidge

Her

bert

Hoo

ver

Fra

nklin

D. R

oose

velt

Har

ry S

. Tru

man

Dw

ight

D. E

isen

how

erJo

hn F

. Ken

nedy

Lynd

on B

. Joh

nson

Ric

hard

Nix

onG

eral

d R

. For

dJi

mm

y C

arte

r

Ron

ald

Rea

gan

Geo

rge

Bus

h

Will

iam

J. C

linto

n

Geo

rge

W. B

ush

year

vale

nce

(v)

1790 1800 1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20105.2

5.4

5.6

5.8

6

6.2

6.4

6.6

6.8

7

7.2

Geo

rge

Was

hing

ton

[1]

Geo

rge

Was

hing

ton

[2]

John

Ada

ms

Tho

mas

Jef

fers

on [1

]

Tho

mas

Jef

fers

on [2

]

Jam

es M

adis

on [1

]

Jam

es M

adis

on [2

]

Jam

es M

onro

e [1

]

Jam

es M

onro

e [2

]

John

Qui

ncy

Ada

ms

And

rew

Jac

kson

[1]

And

rew

Jac

kson

[2]

Mar

tin v

an B

uren

John

Tyl

er

Jam

es K

. Pol

kZ

acha

ry T

aylo

rM

illar

d F

illm

ore

Fra

nklin

Pie

rce

Jam

es B

ucha

nan

Abr

aham

Lin

coln

And

rew

Joh

nson

Uly

sses

S. G

rant

[1]

Uly

sses

S. G

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.74 of 155

labMT 1.0:language assessment by Mechanical Turk

Four corpora: Twitter Google Books Music Lyrics New York

Times

5000 most frequently used words for each corpus. 10,222 words, 50 evaluations each, 1–9 scale: [27]

1 2 3 4 5 6 7 8 9

http://www.uvm.edu



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Introduction

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http://www.uvm.edu



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.76 of 155

valence word valence std dev twitter g-books nyt lyricsrank rank rank rank rank

1 laughter 8.50 0.93 3600 – – 17282 happiness 8.44 0.97 1853 2458 – 12303 love 8.42 1.11 25 317 328 234 happy 8.30 0.99 65 1372 1313 3755 laughed 8.26 1.16 3334 3542 – 23326 laugh 8.22 1.37 1002 3998 4488 6477 laughing 8.20 1.11 1579 – – 11228 excellent 8.18 1.10 1496 1756 3155 –9 laughs 8.18 1.16 3554 – – 285610 joy 8.16 1.06 988 2336 2723 80911 successful 8.16 1.08 2176 1198 1565 –12 win 8.12 1.08 154 3031 776 69413 rainbow 8.10 0.99 2726 – – 172314 smile 8.10 1.02 925 2666 2898 34915 won 8.10 1.22 810 1167 439 149316 pleasure 8.08 0.97 1497 1526 4253 139817 smiled 8.08 1.07 – 3537 – 224818 rainbows 8.06 1.36 – – – 421619 winning 8.04 1.05 1876 – 1426 364620 celebration 8.02 1.53 3306 – 2762 407021 enjoyed 8.02 1.53 1530 2908 3502 –22 healthy 8.02 1.06 1393 3200 3292 461923 music 8.02 1.12 132 875 167 37424 celebrating 8.00 1.14 2550 – – –25 congratulations 8.00 1.63 2246 – – –26 weekend 8.00 1.29 317 – 833 225627 celebrate 7.98 1.15 1606 – 3574 210828 comedy 7.98 1.15 1444 – 2566 –29 jokes 7.98 0.98 2812 – – 380830 rich 7.98 1.32 1625 1221 1469 890

http://www.uvm.edu



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.77 of 155

valence word valence std dev twitter g-books nyt lyricsrank rank rank rank rank

10193 violence 1.86 1.05 4299 1724 1238 201610194 cruel 1.84 1.15 2963 – – 144710195 cry 1.84 1.28 1028 3075 – 22610196 failed 1.84 1.00 2645 1618 1276 292010197 sickness 1.84 1.18 4735 – – 378210198 abused 1.83 1.31 – – – 458910199 tortured 1.82 1.42 – – – 469310200 fatal 1.80 1.53 – 4089 – 372410201 killings 1.80 1.54 – – 4914 –10202 murdered 1.80 1.63 – – – 479610203 war 1.80 1.41 468 175 291 46210204 kills 1.78 1.23 2459 – – 285710205 jail 1.76 1.02 1642 – 2573 161910206 terror 1.76 1.00 4625 4117 4048 237010207 die 1.74 1.19 418 730 2605 14310208 killing 1.70 1.36 1507 4428 1672 99810209 arrested 1.64 1.01 2435 4474 1435 –10210 deaths 1.64 1.14 – – 2974 –10211 raped 1.64 1.43 – – – 452810212 torture 1.58 1.05 3175 – – 312610213 died 1.56 1.20 1223 866 208 82610214 kill 1.56 1.05 798 2727 2572 43010215 killed 1.56 1.23 1137 1603 814 127310216 cancer 1.54 1.07 946 1884 796 380210217 death 1.54 1.28 509 307 373 43310218 murder 1.48 1.01 2762 3110 1541 105910219 terrorism 1.48 0.91 – – 3192 –10220 rape 1.44 0.79 3133 – 4115 297710221 suicide 1.30 0.84 2124 4707 3319 210710222 terrorist 1.30 0.91 3576 – 3026 –

http://www.uvm.edu



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.78 of 155

std dev word valence std dev twitter g-books nyt lyricsrank rank rank rank rank

1 fking 4.64 2.93 448 – – 6202 fkin 3.86 2.74 1077 – – 6883 fked 3.56 2.71 1840 – – 9044 pussy 4.80 2.66 2019 – – 9495 whiskey 5.72 2.64 – – – 22086 slut 3.57 2.63 – – – 40717 cigarettes 3.31 2.60 – – – 32798 fk 4.14 2.58 322 – – 1859 mortality 4.38 2.55 – 3960 – –10 cigarette 3.09 2.52 – – – 267811 motherfkers 2.51 2.47 – – – 146612 churches 5.70 2.46 – 2281 – –13 motherfking 2.64 2.46 – – – 291014 capitalism 5.16 2.45 – 4648 – –15 porn 4.18 2.43 1801 – – –16 summer 6.40 2.39 896 1226 721 59017 beer 5.92 2.39 839 4924 3960 141318 execution 3.10 2.39 – 2975 – –19 wines 6.28 2.37 – – 3316 –20 zombies 4.00 2.37 4708 – – –21 aids 4.28 2.35 2983 3996 1197 –22 capitalist 4.84 2.34 – 4694 – –23 revenge 3.71 2.34 – – – 276624 mcdonalds 5.98 2.33 3831 – – –25 beatles 6.44 2.33 3797 – – –26 islam 4.68 2.33 – 4514 – –27 pay 5.30 2.32 627 769 460 49928 alcohol 5.20 2.32 2787 2617 3752 360029 muthafkin 3.00 2.31 – – – 410730 christ 6.16 2.31 2509 909 4238 1526

http://www.uvm.edu



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Twitter—living in the now:

0 2 4 6 8 10 12 14 16 18 20 22 240

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

hour of day (local time)

coun

t fra

ctio

n

breakfast

lunch

dinner

Quantifying the quotidian.

http://www.uvm.edu



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Twitter—living in the now:

0 2 4 6 8 10 12 14 16 18 20 22 240

0.01

0.02

0.03

0.04

0.05

0.06

0.07


coun

t fra

ctio

n

hungry

starving

food

eat

chicken

Makes the unexpected believable...

http://www.uvm.edu


The happiest distribution:

2 4 6 10 26 100 14010

0

102

104

106

Number of Letters (String Length)

Fre

quen

cy

haha

ha hahaha

hahahahaha

hahahahahahahahahahahahaha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

haha

Work by Tyler Gray et al., unpublished.


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http://www.uvm.edu



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.83 of 155

http://www.uvm.edu


Individual tweets have all kinds of potentialimpact:

Meme come true: Fleetwood Mac re-enter UScharts thanks to Twitter post (2018/04/04)

Trump’s tweets (Amazon is a buy?)

https://www.theguardian.com/music/2018/apr/04/fleetwood-mac-re-enter-us-charts-dreams-twitter-tweet-billboard

https://www.theguardian.com/music/2018/apr/04/fleetwood-mac-re-enter-us-charts-dreams-twitter-tweet-billboard

https://www.marketwatch.com/story/trump-isnt-tweeting-nearly-enough-these-amazon-stock-investors-say-2018-04-04


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Mentions of CIA on Twitter, end of 2011:

See story here for example [slate].

http://www.uvm.edu


http://slatest.slate.com/posts/2011/11/04/cia_s_vengeful_librarians_track_up_to_5_million_tweets_per_day.html


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The daily unravelling of the human mind:

midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight5.9

5.95

6

6.05

6.1

6.15

Local time

Aver

age

happin

ess

havg 2010

Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday

“Social Scientists wade into the Tweet stream” byGreg Miller,Science, 333, 1814–1815, 2011 [28]

http://www.uvm.edu



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The daily unravelling of the human mind:

midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight5.9

5.95

6

6.05

6.1

6.15

Local time

Aver

age

happin

ess

havg 2010

Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday

0 2 4 6 8 10 12 14 16 18 20 22 240

0.01

0.02

0.03

0.04

0.05

0.06


coun

t (%

)

“Social Scientists wade into the Tweet stream” byGreg Miller,Science, 333, 1814–1815, 2011 [28]

http://www.uvm.edu



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−10 −5 0 5 10

1

5

10

15

20

25

30

35

40

45

50

love +↑no −↓

haha +↑party +↑

fun +↑saturday +↑

+↓newweekend +↑not −↓happy +↑dont −↓

−↑lasthahaha +↑

−↑bored−↑drunk

live +↑die −↓friends +↑game +↑con −↓movie +↑cant −↓

−↑fightbirthday +↑

+↓googlegreat +↑sunday +↑family +↑beautiful +↑beach +↑home +↑

+↓lunchsick −↓shopping +↑playing +↑

−↑don’tamazing +↑bad −↓awesome +↑homework −↓wedding +↑

−↑hangover−↑miss+↓free

shit −↓court −↓nice +↑won +↑

+↓schoolmovies +↑


Wor

dra

nkr

Tref: Tuesdays (havg=6.03)Tcomp: Saturdays (havg=6.06)

Text size:Tref Tcomp

+↓ +↑

−↑ −↓

Balance:

−87 : +187

0 100

100

101

102

103

104

∑r

i=1 δhavg,i

http://www.uvm.edu



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Text element and context correlate in happinessscores: Compare ambient happiness with text element

happiness. Spearman correlation coefficient:𝑟𝑠 ≃ 0.79, 𝑝-value < 10−10. An on-average result: says nothing about any

individual sentence. Extra random piece: stemming is fallible.

http://www.uvm.edu


Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug5.8

5.9

6

6.1

6.2

6.3

6.4

09/29

10/31

11/27

12/24

12/25

12/31

01/0102/14

04/12

04/27

06/21

06/25

07/04

08/0609/14

10/31

11/2612/24

12/25

12/31

01/01

02/14

02/27

04/0405/09

05/24

06/20

06/27

07/04

11/25

12/24

12/25

12/31

01/0102/14

03/11

04/24

04/29

05/02

05/08

06/19

07/05

07/23 08/0808/23

ave

rage

happin

ess

havg

A

2008— 2009— 2010— 2011—MondayTuesdayWednesdayThursdayFridaySaturdaySunday

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

300

400

500

600

700

Sim

pso

nle

xica

lsize

NS

B

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug0

1

2

3

date

word

count

(x10

7)

C

−20 −10 0 10 20

1

5

10

15

20

25

30

35

40

45

50

−↑bailout−↑bill

−↑down−↑failed

−↑notlast −↓

−↑no−↑fail

−↑fails−↑blame

+↓love−↑failure

−↑bad−↑don’t

−↑against−↑die

−↑rejected−↑depression

−↑crisismoney +↑

+↓sunday+↓fun

+↓party+↓game

weekend +↑+↓won

old −↓−↑worst+↓great−↑panic

+↓awesome−↑didn’t

+↓google+↓saturday

billion +↑−↑hurt+↓win

cancer −↓−↑sick

−↑problem−↑crash+↓friday−↑falling

house +↑+↓home−↑killed

−↑fearmiss −↓

−↑gossip−↑poor


Wor

dra

nkr

Tref: 7 days before and after (havg=6.00)Tcomp: Monday, 2008/09/29 (havg=5.95)

Bailout of the U.S. financial system:

A


+↓ +↑

−↑ −↓

Balance:

−165 : +65

−100 0

100

101

102

103

104

∑r

i=1 δhavg,i

−30 −20 −10 0 10 20 30

1

5

10

15

20

25

30

35

40

45

50

wedding +↑dead −↓

dont −↓death −↓beautiful +↑hate −↓kiss +↑prince +↑

+↓easter+↓happy

no −↓princess +↑never −↓shit −↓killed −↓not −↓dress +↑real +↑

+↓me+↓good

+↓youweekend +↑friday +↑party +↑

+↓chocolate+↓love+↓win

married +↑+↓game

live +↑bad −↓congrats +↑amazing +↑kill −↓ill −↓nigga −↓wow +↑died −↓

+↓lifeass −↓hell −↓gorgeous +↑congratulations +↑

+↓lolcouple +↑

+↓friendskissed +↑she +↑killing −↓

+↓haha


Wor

dra

nk

r

Tref: 7 days before and after (havg=5.98)Tcomp: Friday, 2011/04/29 (havg=6.04)

Royal Wedding of Prince William & Catherine Middleton:

B


+↓ +↑

−↑ −↓

Balance:

−68 : +168

0 100

100

101

102

103

104

∑r

i=1 δhavg,i

−20 −10 0 10 20

1

5

10

15

20

25

30

35

40

45

50

−↑dead−↑death

−↑killed+↓love

−↑not−↑kill

−↑died−↑killing+↓happy

−↑saddam+↓haha

+↓me−↑war

+↓mothers−↑terrorist

−↑bad−↑pakistan

−↑buriedno −↓

−↑hussein+↓hahaha

−↑terror+↓like

+↓wedding−↑terrorism

−↑enemy+↓lol

−↑shotcelebrating +↑ill −↓

−↑hellcelebrate +↑

+↓mom−↑loss

−↑mournamerica +↑

+↓you−↑deaths

1st +↑sea +↑usa +↑

+↓beautifulpeace +↑

+↓fun−↑evil

+↓good−↑attack

we +↑−↑breaking

−↑burial


Wor

dra

nkr

Tref: 7 days before and after (havg=5.98)Tcomp: Monday, 2011/05/02 (havg=5.89)

Death of Osama Bin Laden:

C


+↓ +↑

−↑ −↓

Balance:

−143 : +43

−100 0

100

101

102

103

104

∑r

i=1 δhavg,i


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Introduction

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.91 of 155

Ambient happiness:Word h

(amb)avg Total Tweets h

(norm)avg

1. happy +0.430 1.65e+07 (13) +1.104 (1)2. Christmas +0.404 4.89e+06 (35) +0.953 (3)3. vegan +0.315 1.84e+05 (90) -0.015 (46)4. :) +0.274 1.04e+07 (20) +0.630 (12)5. family +0.251 5.01e+06 (32) +0.716 (7)6. :-) +0.228 1.67e+06 (60) +0.560 (16)7. our +0.207 1.41e+07 (16) +0.159 (33)8. win +0.204 7.98e+06 (26) +0.924 (4)9. vacation +0.200 9.35e+05 (67) +0.817 (5)10. party +0.170 6.44e+06 (29) +0.679 (9)11. love +0.164 4.67e+07 (6) +0.977 (2)12. friends +0.155 7.67e+06 (27) +0.685 (8)13. hope +0.149 1.18e+07 (18) +0.515 (19)14. coffee +0.147 2.80e+06 (46) +0.518 (18)15. cash +0.146 1.28e+06 (63) +0.601 (14)16. sun +0.144 2.39e+06 (52) +0.737 (6)17. income +0.137 5.10e+05 (76) +0.621 (13)18. summer +0.135 3.00e+06 (43) +0.221 (29)19. church +0.131 1.81e+06 (58) -0.016 (47)20. Valentine +0.127 2.47e+05 (84) +0.593 (15)21. Stephen Colbert +0.126 2.38e+04 (99) +0.001 (45)22. USA +0.113 2.16e+06 (54) +0.325 (26)23. ! +0.106 3.44e+06 (40) +0.195 (31)24. winter +0.101 1.26e+06 (64) +0.050 (43)25. God +0.099 8.58e+06 (25) +0.468 (20)26. hot +0.095 7.12e+06 (28) -0.172 (54)27. ;) +0.094 2.61e+06 (48) +0.326 (25)28. Jesus +0.094 2.03e+06 (56) +0.247 (28)29. today +0.092 2.56e+07 (9) +0.126 (36)30. kiss +0.072 1.70e+06 (59) +0.632 (11)31. yes +0.056 1.16e+07 (19) +0.321 (27)32. tomorrow +0.054 1.04e+07 (21) +0.086 (38)33. you +0.052 1.73e+08 (3) +0.111 (37)34. heaven +0.041 7.42e+05 (71) +0.674 (10)35. ;-) +0.041 9.39e+05 (66) +0.395 (23)36. we +0.035 3.91e+07 (7) +0.146 (34)37. yesterday +0.033 3.08e+06 (42) -0.168 (53)38. dark +0.031 1.58e+06 (61) -0.766 (81)39. ? +0.030 2.32e+06 (53) -0.503 (68)40. RT +0.028 3.39e+08 (1) -0.443 (66)41. Michael Jackson +0.018 8.26e+05 (70) -0.213 (59)42. night +0.014 1.71e+07 (12) +0.074 (40)43. life +0.012 1.40e+07 (17) +0.422 (22)44. health -0.000 2.58e+06 (50) +0.447 (21)45. sex -0.008 3.55e+06 (39) +0.542 (17)46. work -0.010 1.84e+07 (11) -0.174 (56)47. girl -0.010 1.01e+07 (22) +0.331 (24)48. boy -0.026 4.93e+06 (33) +0.062 (41)49. I -0.048 3.08e+08 (2) -0.062 (49)50. commute -0.048 9.01e+04 (94) -0.206 (57)

Word h(amb)avg Total Tweets h

(norm)avg

51. snow -0.051 2.60e+06 (49) +0.083 (39)52. Jon Stewart -0.052 5.21e+04 (97) -0.024 (48)53. school -0.056 9.26e+06 (24) +0.050 (42)54. Lehman Brothers -0.078 8.50e+03 (100) -0.721 (79)55. them -0.090 1.54e+07 (15) -0.280 (60)56. right -0.090 1.92e+07 (10) +0.126 (35)57. woman -0.115 2.54e+06 (51) +0.202 (30)58. left -0.118 4.89e+06 (34) -0.383 (63)59. me -0.119 1.44e+08 (4) +0.160 (32)60. election -0.127 5.60e+05 (75) -0.306 (61)61. Sarah Palin -0.128 2.26e+05 (87) -0.681 (76)62. no -0.132 9.51e+07 (5) -1.415 (90)63. rain -0.134 3.23e+06 (41) +0.050 (44)64. climate -0.135 3.64e+05 (80) -0.160 (51)65. gay -0.152 2.73e+06 (47) -0.552 (72)66. lose -0.157 2.06e+06 (55) -1.181 (86)67. they -0.159 2.74e+07 (8) -0.208 (58)68. oil -0.162 1.38e+06 (62) -0.411 (65)69. cold -0.162 3.67e+06 (36) -0.546 (71)70. I feel -0.173 5.17e+06 (31) -0.129 (50)71. man -0.175 1.59e+07 (14) -0.163 (52)72. Republican -0.181 2.30e+05 (86) -0.539 (70)73. sad -0.187 3.56e+06 (38) -1.366 (89)74. gas -0.193 1.02e+06 (65) -0.471 (67)75. economy -0.203 6.09e+05 (73) -0.525 (69)76. Obama -0.205 2.98e+06 (44) -0.173 (55)77. Democrat -0.226 9.32e+04 (93) -0.384 (64)78. Congress -0.231 3.92e+05 (79) -0.580 (74)79. hell -0.250 6.27e+06 (30) -1.551 (96)80. sick -0.262 3.58e+06 (37) -1.630 (97)81. Muslim -0.262 2.15e+05 (88) -0.569 (73)82. war -0.270 1.96e+06 (57) -2.040 (100)83. Pope -0.277 1.52e+05 (91) -0.316 (62)84. hate -0.282 9.65e+06 (23) -1.520 (94)85. Glenn Beck -0.282 1.14e+05 (92) -0.776 (82)86. Islam -0.299 1.87e+05 (89) -0.710 (78)87. George Bush -0.333 3.23e+04 (98) -0.747 (80)88. Goldman Sachs -0.337 5.27e+04 (96) -0.984 (84)89. depressed -0.339 2.81e+05 (82) -1.541 (95)90. Senate -0.340 4.48e+05 (78) -0.601 (75)91. BP -0.355 5.82e+05 (74) -0.902 (83)92. gun -0.367 6.81e+05 (72) -1.476 (93)93. drugs -0.382 5.10e+05 (77) -1.452 (91)94. headache -0.437 8.57e+05 (69) -1.881 (98)95. :-( -0.455 3.40e+05 (81) -1.174 (85)96. :( -0.472 2.89e+06 (45) -1.288 (88)97. Afghanistan -0.703 2.74e+05 (83) -1.458 (92)98. mosque -0.709 6.98e+04 (95) -0.694 (77)99. flu -0.735 9.01e+05 (68) -1.912 (99)100. Iraq -0.773 2.39e+05 (85) -1.282 (87)

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Simpson lexical size, 𝑁S:Word h

(amb)avg Total Tweets h

(norm)avg

1. happy +0.430 1.65e+07 (13) +1.104 (1)2. Christmas +0.404 4.89e+06 (35) +0.953 (3)3. vegan +0.315 1.84e+05 (90) -0.015 (46)4. :) +0.274 1.04e+07 (20) +0.630 (12)5. family +0.251 5.01e+06 (32) +0.716 (7)6. :-) +0.228 1.67e+06 (60) +0.560 (16)7. our +0.207 1.41e+07 (16) +0.159 (33)8. win +0.204 7.98e+06 (26) +0.924 (4)9. vacation +0.200 9.35e+05 (67) +0.817 (5)10. party +0.170 6.44e+06 (29) +0.679 (9)11. love +0.164 4.67e+07 (6) +0.977 (2)12. friends +0.155 7.67e+06 (27) +0.685 (8)13. hope +0.149 1.18e+07 (18) +0.515 (19)14. coffee +0.147 2.80e+06 (46) +0.518 (18)15. cash +0.146 1.28e+06 (63) +0.601 (14)16. sun +0.144 2.39e+06 (52) +0.737 (6)17. income +0.137 5.10e+05 (76) +0.621 (13)18. summer +0.135 3.00e+06 (43) +0.221 (29)19. church +0.131 1.81e+06 (58) -0.016 (47)20. Valentine +0.127 2.47e+05 (84) +0.593 (15)21. Stephen Colbert +0.126 2.38e+04 (99) +0.001 (45)22. USA +0.113 2.16e+06 (54) +0.325 (26)23. ! +0.106 3.44e+06 (40) +0.195 (31)24. winter +0.101 1.26e+06 (64) +0.050 (43)25. God +0.099 8.58e+06 (25) +0.468 (20)26. hot +0.095 7.12e+06 (28) -0.172 (54)27. ;) +0.094 2.61e+06 (48) +0.326 (25)28. Jesus +0.094 2.03e+06 (56) +0.247 (28)29. today +0.092 2.56e+07 (9) +0.126 (36)30. kiss +0.072 1.70e+06 (59) +0.632 (11)31. yes +0.056 1.16e+07 (19) +0.321 (27)32. tomorrow +0.054 1.04e+07 (21) +0.086 (38)33. you +0.052 1.73e+08 (3) +0.111 (37)34. heaven +0.041 7.42e+05 (71) +0.674 (10)35. ;-) +0.041 9.39e+05 (66) +0.395 (23)36. we +0.035 3.91e+07 (7) +0.146 (34)37. yesterday +0.033 3.08e+06 (42) -0.168 (53)38. dark +0.031 1.58e+06 (61) -0.766 (81)39. ? +0.030 2.32e+06 (53) -0.503 (68)40. RT +0.028 3.39e+08 (1) -0.443 (66)41. Michael Jackson +0.018 8.26e+05 (70) -0.213 (59)42. night +0.014 1.71e+07 (12) +0.074 (40)43. life +0.012 1.40e+07 (17) +0.422 (22)44. health -0.000 2.58e+06 (50) +0.447 (21)45. sex -0.008 3.55e+06 (39) +0.542 (17)46. work -0.010 1.84e+07 (11) -0.174 (56)47. girl -0.010 1.01e+07 (22) +0.331 (24)48. boy -0.026 4.93e+06 (33) +0.062 (41)49. I -0.048 3.08e+08 (2) -0.062 (49)50. commute -0.048 9.01e+04 (94) -0.206 (57)

Word h(amb)avg Total Tweets h

(norm)avg

51. snow -0.051 2.60e+06 (49) +0.083 (39)52. Jon Stewart -0.052 5.21e+04 (97) -0.024 (48)53. school -0.056 9.26e+06 (24) +0.050 (42)54. Lehman Brothers -0.078 8.50e+03 (100) -0.721 (79)55. them -0.090 1.54e+07 (15) -0.280 (60)56. right -0.090 1.92e+07 (10) +0.126 (35)57. woman -0.115 2.54e+06 (51) +0.202 (30)58. left -0.118 4.89e+06 (34) -0.383 (63)59. me -0.119 1.44e+08 (4) +0.160 (32)60. election -0.127 5.60e+05 (75) -0.306 (61)61. Sarah Palin -0.128 2.26e+05 (87) -0.681 (76)62. no -0.132 9.51e+07 (5) -1.415 (90)63. rain -0.134 3.23e+06 (41) +0.050 (44)64. climate -0.135 3.64e+05 (80) -0.160 (51)65. gay -0.152 2.73e+06 (47) -0.552 (72)66. lose -0.157 2.06e+06 (55) -1.181 (86)67. they -0.159 2.74e+07 (8) -0.208 (58)68. oil -0.162 1.38e+06 (62) -0.411 (65)69. cold -0.162 3.67e+06 (36) -0.546 (71)70. I feel -0.173 5.17e+06 (31) -0.129 (50)71. man -0.175 1.59e+07 (14) -0.163 (52)72. Republican -0.181 2.30e+05 (86) -0.539 (70)73. sad -0.187 3.56e+06 (38) -1.366 (89)74. gas -0.193 1.02e+06 (65) -0.471 (67)75. economy -0.203 6.09e+05 (73) -0.525 (69)76. Obama -0.205 2.98e+06 (44) -0.173 (55)77. Democrat -0.226 9.32e+04 (93) -0.384 (64)78. Congress -0.231 3.92e+05 (79) -0.580 (74)79. hell -0.250 6.27e+06 (30) -1.551 (96)80. sick -0.262 3.58e+06 (37) -1.630 (97)81. Muslim -0.262 2.15e+05 (88) -0.569 (73)82. war -0.270 1.96e+06 (57) -2.040 (100)83. Pope -0.277 1.52e+05 (91) -0.316 (62)84. hate -0.282 9.65e+06 (23) -1.520 (94)85. Glenn Beck -0.282 1.14e+05 (92) -0.776 (82)86. Islam -0.299 1.87e+05 (89) -0.710 (78)87. George Bush -0.333 3.23e+04 (98) -0.747 (80)88. Goldman Sachs -0.337 5.27e+04 (96) -0.984 (84)89. depressed -0.339 2.81e+05 (82) -1.541 (95)90. Senate -0.340 4.48e+05 (78) -0.601 (75)91. BP -0.355 5.82e+05 (74) -0.902 (83)92. gun -0.367 6.81e+05 (72) -1.476 (93)93. drugs -0.382 5.10e+05 (77) -1.452 (91)94. headache -0.437 8.57e+05 (69) -1.881 (98)95. :-( -0.455 3.40e+05 (81) -1.174 (85)96. :( -0.472 2.89e+06 (45) -1.288 (88)97. Afghanistan -0.703 2.74e+05 (83) -1.458 (92)98. mosque -0.709 6.98e+04 (95) -0.694 (77)99. flu -0.735 9.01e+05 (68) -1.912 (99)100. Iraq -0.773 2.39e+05 (85) -1.282 (87)

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Ambient happiness:

09/09/08 12/01/08 03/01/09 06/01/09 09/01/09−1.2

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

date

h avg

(am

b)

HappyTea Party

:)!

:(Afghanistan

Sad

09/09/08 12/01/08 03/01/09 06/01/09 09/01/09−5

−4

−3

−2

−1

date

log 10

rel f

req

A

B

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0 20 40 60 80 1000

10

20

30

40

50

60

70

80

90

100S

imps

on le

xica

l siz

e (N

S)

rank

ambient happiness (havg(amb)) rank

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10/01/09 12/01/09 02/01/10 04/01/10 06/01/10−1

−0.8

−0.6

−0.4

−0.2

0

0.2 A

date

h avg

(am

b)

Tiger Woods

09/01/09 12/01/09 03/01/10 05/31/10−5

−4

−3

date

log 10

rel f

req

01/01/10 03/01/10 05/01/10 07/01/10−1

−0.8

−0.6

−0.4

−0.2

0

0.2 B

date

h avg

(am

b)

BP

12/01/09 03/01/10 05/31/10−5

−4

−3

date

log 10

rel f

req

−20 0 20

1

5

10

15

20

25

−↑accident−↑crash

−↑injuredno −↓

−↑scandal+↓love

−↑hospital−↑cheating

wife +↑−↑alleged

car +↑+↓me

santa +↑−↑cheated

ill −↓−↑cheat

sex +↑+↓haha−↑alone

−↑not+↓happy

−↑breaking−↑divorce

movie +↑+↓good

Per word average happiness shi ft δhavg, r (%)

Word

rank

r

Tref: All Tweets (havg=6.06)Tcomp: Tiger Woods (havg=5.74)

Text size:Tre f Tcomp

+↓ +↑

−↑ −↓

Ba lance:−188 : +88

−100 0

100

101

102

103

104

C

∑r

i=1δhavg, i

−20 −10 0 10 20

1

5

10

15

20

25

−↑disasterno −↓

−↑down−↑shut

−↑kill+↓love−↑fake

−↑stop+↓me

+↓haha−↑not+↓lol

ill −↓+↓hahaha

dont −↓−↑died

billion +↑con −↓

−↑blame−↑criminal

+↓happy−↑damage

−↑costs−↑fails

−↑crisis


Word

rank

r

Tref: All Tweets (havg=6.04)Tcomp: BP (havg=5.57)


+↓ +↑

−↑ −↓

Ba lance:−167 : +67

−100 0

100

101

102

103

104

D

∑r

i=1δhavg, i

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10/01/09 12/01/09 02/01/10 04/01/10 06/01/10−1

−0.8

−0.6

−0.4

−0.2

0

0.2 A

date

h avg

(am

b)

Tiger Woods

09/01/09 12/01/09 03/01/10 05/31/10−5

−4

−3

date

log 10

rel f

req

01/01/10 03/01/10 05/01/10 07/01/10−1

−0.8

−0.6

−0.4

−0.2

0

0.2 B

date

h avg

(am

b)

BP

12/01/09 03/01/10 05/31/10−5

−4

−3

date

log 10

rel f

req

−20 0 20

1

5

10

15

20

25

−↑accident−↑crash

−↑injuredno −↓

−↑scandal+↓love

−↑hospital−↑cheating

wife +↑−↑alleged

car +↑+↓me

santa +↑−↑cheated

ill −↓−↑cheat

sex +↑+↓haha−↑alone

−↑not+↓happy

−↑breaking−↑divorce

movie +↑+↓good


Word

rank

r

Tref: All Tweets (havg=6.06)Tcomp: Tiger Woods (havg=5.74)


+↓ +↑

−↑ −↓

Ba lance:−188 : +88

−100 0

100

101

102

103

104

C

∑r

i=1δhavg, i

−20 −10 0 10 20

1

5

10

15

20

25

−↑disasterno −↓

−↑down−↑shut

−↑kill+↓love−↑fake

−↑stop+↓me

+↓haha−↑not+↓lol

ill −↓+↓hahaha

dont −↓−↑died

billion +↑con −↓

−↑blame−↑criminal

+↓happy−↑damage

−↑costs−↑fails

−↑crisis


Word

rank

r

Tref: All Tweets (havg=6.04)Tcomp: BP (havg=5.57)


+↓ +↑

−↑ −↓

Ba lance:−167 : +67

−100 0

100

101

102

103

104

D

∑r

i=1δhavg, i

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02/01/10 04/01/10 06/01/10 08/01/10−1

−0.8

−0.6

−0.4

−0.2

0

0.2 A

date

h avg

(am

b)

Pope

03/01/10 05/31/10−5

−4

−3

date

log 10

rel f

req

11/01/08 01/01/09 03/01/09 05/01/09−1

−0.8

−0.6

−0.4

−0.2

0

0.2 B

date

h avg

(am

b)

Israel

09/09/08 12/01/08 03/01/09−5

−4

−3

date

log 10

rel f

req

−50 0 50

1

5

10

15

20

25

−↑abuse−↑scandal

sex +↑−↑victims

−↑arrestno −↓

−↑against+↓love

−↑arrestedchild +↑

−↑not−↑resignation

+↓me+↓haha−↑crisis

+↓loldont −↓

−↑crimes−↑attacks

−↑accused−↑deaf

−↑down+↓hahaha

−↑evil−↑rape


Word

rank

r

Tref: All Tweets (havg=6.04)Tcomp: Pope (havg=5.40)


+↓ +↑

−↑ −↓

Ba lance:−157 : +57

−100 0

100

101

102

103

104

C

∑r

i=1δhavg, i

−10 0 10

1

5

10

15

20

25

−↑war−↑fired

−↑against−↑killed

−↑attacks−↑attack

−↑offensive−↑fire

−↑crimespeace +↑

−↑no−↑not−↑die

−↑killing−↑stop+↓love

−↑bombs−↑fighting

−↑kill−↑kills

−↑conflict−↑death

−↑terrorist−↑weapons−↑bombing


Word

rank

r

Tref: All Tweets (havg=6.05)Tcomp: Israel (havg=5.30)


+↓ +↑

−↑ −↓

Ba lance:−131 : +31

−100 0

100

101

102

103

104

D

∑r

i=1δhavg, i

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Public Opinion Polling with Twitter

Emily M. Cody,1, ∗ Andrew J. Reagan,1, † Peter Sheridan Dodds,1, ‡ and Christopher M. Danforth1, §

1Department of Mathematics & Statistics, Vermont Complex Systems Center,

Computational Story Lab, & the Vermont Advanced Computing Core,

The University of Vermont, Burlington, VT 05401.

(Dated: August 9, 2016)

Solicited public opinion surveys reach a limited subpopulation of willing participants and areexpensive to conduct, leading to poor time resolution and a restricted pool of expert-chosen surveytopics. In this study, we demonstrate that unsolicited public opinion polling through sentimentanalysis applied to Twitter correlates well with a range of traditional measures, and has predictivepower for issues of global importance. We also examine Twitter’s potential to canvas topics seldomsurveyed, including ideas, personal feelings, and perceptions of commercial enterprises. Two of ourmajor observations are that appropriately filtered Twitter sentiment (1) predicts President Obama’sjob approval three months in advance, and (2) correlates well with surveyed consumer sentiment.To make possible a full examination of our work and to enable others’ research, we make public over10,000 data sets, each a seven-year series of daily word counts for tweets containing a frequentlyused search term.

I. INTRODUCTION

Public opinion data can be used to determine pub-lic awareness, to predict outcomes of events, and to infercharacteristics of human behaviors. Indeed, readily avail-able public opinion data is valuable to researchers, poli-cymakers, marketers, and many other groups, but is dif-ficult to generate. Solicited polls can be expensive, pro-hibitively time consuming, and may only reach a limitednumber of people on a limited number of days. Polling ac-curacy evidently relies on accessing representative popu-lations and high response rates. Poor temporal samplingwill weaken any poll’s value as individual opinions varyin time and in response to social influence[12, 43].

With the continued rise of social media as a communi-cation platform, the ability to construct unsolicited pub-lic opinion polls has become a possibility for researchersthough parsing of massive text-based datasets. Socialmedia provides extraordinary access to public expressionsin real time, and has been shown to play a role in humanbehavior [23].

With its open platform, Twitter has proved to be aboon for many research enterprises [37], having been usedto explore a variety of social and linguistic phenomena[10, 30, 31]; harnessed as a data source to create anearthquake reporting system in Japan [42]; made pos-sible detection of influenza outbreaks [7]; and used toanalyze overall public health [38]. Predictions made us-ing Twitter have focused on elections [22, 44], the spreadof disease [41], crime [46], and the stock market [35].These studies demonstrate a proof-of-concept, avoidingthe more difficult task of building operational systemsfor continued forecasting.

∗ [email protected]† [email protected]‡ [email protected]§ [email protected]

We must be clear that for all its promise, predictionvia social media is difficult. Indeed, we have seen a num-ber of high profile failures such as Google Flu trends [29]and various attempts to predict election outcomes [21].OpinionFinder was used in [9] to evaluate tweets contain-ing direct expressions of emotion as in ‘I feel’, or ‘I amfeeling’, and shown not to have predictive power for thestock market

Despite limitations which we address later in Sec. IV,Twitter data reveals an unprecedented view of humanbehavior and opinion related to major issues of globalimportance [32]. In a previous study [14], we analyzedthe sentiment surrounding climate change conversationon Twitter. We discovered that sentiment varies in re-sponse to climate change news and events, and that theconversation is dominated by activists. Another studyby Helmuth et al. analyzed tweets by United States Sen-ators to determine which research oriented science orga-nizations and which senators are best at getting science-related findings into the hands of the general public [24].Twitter is also often used to analyze public opinion ofpolitical issues [8, 17, 45], and in several previous worksas an opinion polling resource. In an application usingneural networks called TrueHappiness, users enter one of300,000 words to obtain a sentiment estimation based onthis word’s usage in a massive Wikipedia data set, andon previously collected sentiment scores for 10,222 wordson Amazon’s Mechanical Turk [16, 18], hereafter referredto as the labMT word set In another application calledRACCOON, users are invited to enter a query term anda rough sketch to obtain words or phrases on Twitterthat correlate well with the inputs [6]. Google Correlateis a similar tool that discovers Google searches for termsor phrases that match well with real-world time series [3].Financial term searches from Google Trends was shownby Preis et al. [39] to correlate with Dow Jones economicindices.

We argue that Twitter is a better source for opin-ion mining than Wikipedia, used in TrueHappiness, due

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“Public opinion polling with Twitter”Cody et al.,Available online athttps://arxiv.org/abs/1608.02024, 2016. [4]

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http://www.uvm.edu/pdodds/research/papers/others/everything/cody2016b.pdf

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Twitter’s Feels predict Obama’s Approval Rating:

−5 0 5

1

5

10

15

20

25

30

35

40

45

50

−↑no−↑war−↑iraq

−↑against−↑not

−↑illegal+↓new

−↑crisis−↑fight

tax −↓−↑threat−↑killed+↓hope

+↓first−↑terrorist

−↑deathbill −↓

−↑worstbailout −↓

−↑stop−↑con+↓love

+↓goodamerica +↑

−↑destroy−↑killing−↑attack

−↑lawsuit−↑never

vacation +↑taxes −↓

+↓great−↑crying

−↑breakingafghanistan −↓

−↑hatedie −↓

−↑violencethanks +↑fail −↓deficit −↓

−↑doesn’t−↑shit−↑kill

−↑courtlast −↓

−↑bombinglike +↑

−↑murderyou +↑

Per word average happiness shif t δh av g, r (%)

Wordrankr

T r e f : Obama Q1 (havg=5.92)Tcom p: Obama Q23 (havg=5.53)

Text si z e :T r e f T c omp

+↓ +↑

−↑ −↓

Balanc e :

−154 : +54

−100 0

100

101

102

103

104

∑ri=1δ hav g, i

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.100 of 155

Ambient happiness for “Obama”:

Birthday

Presidential election Presidential election

Nobel Prize

H1N1 emergency

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Introduction

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.101 of 155

Ambient happiness for “Walmart”:

Free gift card

Employee trampled Tracy Morgan sues

Black Friday

A

Shootings

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Introduction

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Hedonometer

Analysis

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.102 of 155

Ambient happiness for “snow”:

B C

Jon Snow dies on GOT

g

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.....

.103 of 155

Dunbar number action:

0 1 2 3 4 5 66.3

6.35

6.4

6.45

6.5

mea

n va

lenc

e v

0 1 2 3 4 5 6250

260

270

280

290

300

310

N e

q

0 1 2 3 4 5 65

6

7

8

log10

follower number per tweet

log 10

cou

nt

# ANEW words

Early Twitter data—follower counts are not worthso much.

Unpublished.

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.104 of 155

392 C.A. Bliss et al. / Journal of Computational Science 3 (2012) 388–397

Fig. 5. A visualization of the 162,445 nodes in the reciprocal reply network for the week beginning December 9, 2008 (Week 14) is depicted. Node colors represent connected

components, a total of 15,342, with the giant component (shown in blue) comprising 76 % of all nodes. The size of each node is proportional to its degree. The visualization

was made using Gephi [39]. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

from a power law using the Kolmogorov–Smirnov test and find no

evidence against the null hypothesis for the week (D = 2.28 × 10−2,

p = 0.095, n = 203,852). We find the same exponent and statistically

stronger evidence of a power law for a sample month (see Fig. A1).

This suggests that these distributions’ tails may be fit by a power

law.

3.2. Measuring happiness

The application of the hedonometer gives reasonable results

when applied to a large body of text, but can be misleading when

applied to smaller units of language [11]. To provide a sense of how

sensitive this measure is to the number of labMT words posted

by users, we sampled happiness–happiness pairs, (hvi, hvj

) whose

respective users, vi and vj , had posted at least total labMT words

during a sample week (week beginning January 27, 2009). For these

users, we compute happiness assortativity and show the varia-

tion with in Fig. 8. For h = 0, there is less variation due to

the numerous words centered around the mean happiness score

regardless of the threshold, ˛. Tuning both parameters too high

results in few sampled words and corrupts the interpretation of the

results.

Figs. 9 and 10 reveal a weakening happiness–happiness corre-

lation for users in the week networks as the path length between

nodes increases. All correlations, for each week, were significant

(p < 10−10). This suggests that the network is assortative with

“Twitter reciprocal reply networks exhibitassortativity with respect to happiness”Bliss, Kloumann, Harris, Danforth, and Dodds.Journal of Computational Science, 3, 388–397,2012. [1]

Decay in happiness correlation in social network.

Not a test of contagion …

http://www.uvm.edu


http://www.uvm.edu/pdodds/research/papers/others/everything/bliss2012a.pdf




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The very surprising tunable hedonometer:

10/01/08 01/01/09 04/01/09 07/01/09 10/01/09 01/01/10 04/01/10 07/01/10 10/01/10 01/01/11

5.4

5.6

5.8

6

6.2

6.4

6.6

6.8

7

7.2

Date

Aver

age

happin

ess

havg

∆havg : A 2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

1 2 3 4 5 6 7 8 90

200

400

600

800

1000

1200 2∆havg

havg

Fre

quen

cy

1

B

0 1 2 30

0.5

1

1.5

2

2.5

3

∆havg

∆h

avg

C

−0.2

0

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0.4

0.6

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1

0 1 2 30

2000

4000

6000

8000

10000

∆havg

#w

ord

s D

0 5000 10000 15000 200000

20

40

60

80

100

word rank

%co

ver

age

F

0 1 2 30

20406080

100

∆havg

%co

ver

age

E

http://www.uvm.edu



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Introduction

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.106 of 155

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.....

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http://www.uvm.edu



Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

SOTU

Geography

Movement

Other Emotions

References

.....

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http://www.uvm.edu



Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

SOTU

Geography

Movement

Other Emotions

References

.....

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Happiness

Introduction

Measurement



Hedonometer

Analysis

Songs

Blogs

SOTU

Geography

Movement

Other Emotions

References

.....

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−4 −2 0 2 4−7

−6

−5

−4

−3

−2

−1

0

lovehahahahahagreatfreehappydie illshit winhate musicfunfriendssongsuperweekendawesomebirthdayhell funnyhahahahaamazingfriendbeautifulpartyfamilysick momcutedead sweetexcitedenjoysadkill smilechristmassunbeach

deathwar chocolateinterestinggiftlovescongratslovedhurt bday wonholidaylaughluckycryfail hahahahahapainworst lovingsuccesspoor

killedfestivalkiss

cancerwonderfulmothergoldpeaceshot

died healthybeautywinnerhurts excellentcake

killingskyvacationjokeenjoyingcelebratelaughingaward

happinesscongratulationsrichpositiveevil fantasticattackfear excitinghilarious

winninghumorjoyhorrible hugscrying successfuldyingbomb deliciousheadache

angry heroangeldiesearnbrilliant

jail enjoyedhugheavenfreedom

easierblessedwars sunshineholidaysentertainmentarrested

tears smilesmurder

hatesfailedbillion

pleasureprize

comedycandyflu

killswinnerskisseskiller romancecrime relax jokes

haters brightincomeaccident cheersworriedfailure

crisis geniusdevil gifts

suicide

bonusfiredvictoryloservirus

diamondlaughsterror

profitfriendlynegative goodnessdangerous b−dayloverscelebrating

steal puppyearthquakedisaster

smilingflowersviolence

slapcelebrationprison

cheatabuse rainbow

disappointedinjuryasshole angels

diseasenightmare

friendshiphating

laughedflowerfunniestcriminal

sadly relaxing

raperacist promotionanger romantic


cruelfuneral

glorypaniccriedpainfulcheatingattacksinjured kissinghated

praiseweekendscupcakesthreat championfloodtheftdepression sweetheart

rosesarrestdivorce miracletasty

depressingsuffering paradise

comforthurtingsuffer

handsomesucceed

stolendepressed cutest

laughter

recession entertainingblessingsachievement

fatal

victimsvictim cupcake

torture

fraudviolent

merrykissed

enemies cakesstealing excitementkillersunemploymentslave

earnings

povertydiamonds

terrorist

finestsuccessfully

poisonbutterflysweetestdestroyed

deaths

glorioussaturdays

outstandingattractionunhappy

raped

crashedhonestycrimesrejected

faithful

terrorism

weaponsbankruptcy

robbedmurderedsickness

tragedykittenmillionaireachievedpeaceful

butterfliespunishmenttsunamicorruptionrobbery delight

sadnessillnessgrandmother

motherfuckertragic

psycho extraordinary

hatreddestructionoptimistic

celebratedsmiled

losses

heartbreaksufferedfrustrationgriefniggerdamagedwounded shines

slaveryhonourthievespollution

abused

drowningsunlightrejection

heavens

diseases sorrow

affectionbombing

cherish

slavessweetness

rainbowssplendid

prisonersmoonlight

holocaustagonyslaughter

drowned

torturedkillings

weepheartache

succeeded

saddenedburial

victoriespeacefully

betray

tumors

succeedingsaddam

∆havg = 2.50

Hour 13, 2010


log10

norm

alize

dword

freq

uen

cyf(∆

havg)

http://www.uvm.edu


Online instrument: hedonometer.org

Machine: @andyreagan

Planned happiness versus tragedies.

http://www.hedonometer.org

http://www.twitter.com/andyreagan

hedonometer.org

Machine: @andyreagan


http://www.twitter.com/andyreagan


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The one reliable source of spontaneous,widespread happiness and despair:

Original tweet

http://www.uvm.edu


https://twitter.com/BreatheSport/status/745702226773970944


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hedonometer.org—word shifts:

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Make your own:

http://www.uvm.edu


http://hedonometer.org/wordshifterator/


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Quokka Labs

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Reagan et al. EPJ Data Science ( 2017) 6:28

DOI 10.1140/epjds/s13688-017-0121-9

REGULAR ART ICLE Open Access

Sentiment analysis methods forunderstanding large-scale texts: a case forusing continuum-scored words and wordshift graphsAndrew J Reagan1*, Christopher M Danforth1, Brian Tivnan2, Jake Ryland Williams3 and

Peter Sheridan Dodds1

*Correspondence:

[email protected] of Vermont, 85 South

Prospect St, Burlington, VT 05405,

USA

Full list of author information is

available at the end of the article

Abstract

The emergence and global adoption of social media has rendered possible the

real-time estimation of population-scale sentiment, an extraordinary capacity which

has profound implications for our understanding of human behavior. Given the

growing assortment of sentiment-measuring instruments, it is imperative to

understand which aspects of sentiment dictionaries contribute to both their

classification accuracy and their ability to provide richer understanding of texts. Here,

we perform detailed, quantitative tests and qualitative assessments of 6

dictionary-based methods applied to 4 different corpora, and briefly examine a

further 20 methods. We show that while inappropriate for sentences,

dictionary-based methods are generally robust in their classification accuracy for

longer texts. Most importantly they can aid understanding of texts with reliable and

meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of

a given text’s lexicon when weighted by word usage frequency; and (2) words are

scored on a continuous scale.

Keywords: sentiment; sentiment analysis; sentiment dictionaries; language; natural

language processing; data visualization; text visualization

1 Introduction

As we move further into what might be called the Sociotechnocene — with increasingly

more interactions, decisions, and impact being made by globally distributed people and

algorithms — the myriad human social dynamics that have shaped our history have be-

come far more visible and measurable than ever before. Of the many ways we are now

able to characterize social systems in microscopic detail, sentiment detection for pop-

ulations at all scales has become a prominent research arena. Attempts to leverage on-

line expression for sentiment mining include prediction of stock markets [–], assessing

responses to advertising, real-time monitoring of global happiness [], and measuring a

health-related quality of life []. The diverse set of instruments produced by this work

now provide indicators that help scientists understand collective behavior, inform pub-

lic policy makers, and, in industry, gauge the sentiment of public response to marketing

© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in anymedium, pro-vided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.

“Sentiment analysis methods for understandinglarge-scale texts: A case for using continuum-scoredwords and word shift graphs”Reagan, Tivnan, Williams, Danforth, and Dodds.EPJ Data Science, 6, , 2017. [31]

Upshots: (1) do use wordshifts, and (2) do not use LIWC ...

http://www.uvm.edu







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Outline

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Happiness in Manhattan:

See Blog post on compstorylab.org

http://www.uvm.edu


http://compstorylab.org/2012/03/22/question-where-is-the-happiest-place-in-new-york-city/


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http://www.uvm.edu



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The Geography of Happiness:

Mitchell et al., PLoS ONE, 2013. [29]

It’s a paper that tweets: @geographyofhapp Online Appendices Much interesting and amusing press ...

Online, interactive US map at hedonometer.org

http://www.uvm.edu


https://twitter.com/geographyofhapp

http://www.uvm.edu/storylab/share/papers/mitchell2013a/

http://www.youtube.com/watch?v=wHbUxvN-eHs

http://www.youtube.com/watch?v=aOrH0owGoIE

http://www.youtube.com/watch?v=4Z2ocFJBGr0

http://hedonometer.org/maps.html


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65 70

5.9

6

6.1

6.2

Gallup well−being

r= 0.511p= 1.50E -4

65 70

3.35

3.4

3.45r= -0. 133p= 3.64E -5

−0.5 0 0.5 162

64

66

68

70r= 0.725p= 2. 71E -9

23462

64

66

68

70r= -0. 586p= 7. 86E -6

0 5 1062

64

66

68

70

Gallu

p w

ell−

bein

g r= -0. 653p= 4. 83E -7

3.35 3.4 3.45

5.9

6

6.1

6.2

BRFSS score

r= 0. 251p= 7. 62E -2

−0.5 0 0.5 1

5.9

6

6.1

6.2

AHR score

r= 0.578p= 1. 10E -5

234

5.9

6

6.1

6.2

Peace index

r= -0. 521p= 1. 04E -4

0 5 10

5.9

6

6.1

6.2

Gun violence

Happin

ess

r= -0. 664p= 1. 99E -7

−0.5 0 0.5 1

3.35

3.4

3.45r= 0.330p= 1. 93E -2

234

3.35

3.4

3.45r= -0. 133p= 3. 59E -1

0 5 10

3.35

3.4

3.45

BR

FS

S s

core

r= -0. 305p= 3. 32E -2

−0.5 0 0.5 1

1.5

2

2.5

3

3.5

4

r= -0. 803p= 2.29E -12

0 5 10

−0.5

0

0.5

1

AH

R s

core

r= -0. 726p= 5. 30E -9

0 5 10

1.5

2

2.5

3

3.5

4

Peace index

r= 0.820p= 1. 06E -12

−12

−10

−8

−6

−4

−2

0

Note: Peace axisis reversed

Happiness: Average word happiness calculated usingLabMT 1.0 and geotagged tweets from 2011

BRFSS score: Average score from the Behavioral RiskFactor Surveillance System survey (2005−2008)

Gallup: Well−being index, based on survey data on lifeevaluation, emotional health, physical health, healthy behavior,work environment and basic access (2011)

Peace index: Composite index of Homicides per100,000 people, violent crimes per 100,000 people, Jailedpopulation per 100,000 people, Police officers per 100,000people,ease of access to small arms (2011)

AHR score: America’s Health Ranking, composite indexof Behavior, Community & Environment, Policy and Clinical Caremetrics (2011)

Gun violence: Shootings per 100,000 people (2011)

FIG. 2: Scatter plot matrix of correlations between different well-being measures. Points are colored by p-value, statistically

insignificant correlations above p = 0.01 are shown in red. Spearman’s r and p-value are reported in the inset.

http://www.uvm.edu



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Happiest Cities:

6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.2

Asheville, NC

Amarillo, TX

Porterville, CA

Boulder, CO

Nashua, NH−−MA

Lafayette, CO

Logan, UT

Gilroy, CA

Davis, CA

Santa Rosa, CA

San Clemente, CA

Simi Valley, CA

Longmont, CO

Idaho Falls, ID

Napa, CA

havg

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Saddest Cities (Sorry Beaumont):

5.88 5.89 5.9 5.91 5.92 5.93

Port Arthur, TX

Waterbury, CT

Montgomery, AL

Dalton, GA

Houma, LA

Alexandria, LA

Texarkana, TX

Lima, OH

Texas City, TX

Rapid City, SD

Flint, MI

Albany, GA

Shreveport, LA

Monroe, LA

Beaumont, TX

havg

http://www.uvm.edu



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−10 −5 0 5 10

1

5

10

15

20

25

30

35

40

45

50

shit −↓

+↓lol

don’t −↓

gift +↑

restaurant +↑

ass −↓

bitch −↓

hate −↓

wine +↑

+↓like

damn −↓

bad −↓

hell −↓

happy +↑

christmas +↑

nice +↑

not −↓

can’t −↓

never −↓

cheers +↑

dont −↓

thank +↑

hope +↑

ain’t −↓

first +↑

lunch +↑

1st +↑

+↓me

+↓haha

you +↑

dinner +↑

+↓new

mad −↓

wrong −↓

food +↑

+↓beach

no −↓

miss −↓

beauty +↑

+↓girl

beautiful +↑

die −↓

deal +↑

hi +↑

bitches −↓

great +↑

coffee +↑

valley +↑

+↓my

−↑sad

Per word ave rage happ iness sh i f t δh av g, r (%)

Word

rankr

Tre f: Al l USA (havg=6.00)Tcomp: Napa, CA (havg=6.26)


+↓ +↑

−↑ −↓

Balan ce :

−84 : +184

0 100

100

101

102

103

104

Pr

i=1δ havg, i

−10 −5 0 5 10

1

5

10

15

20

25

30

35

40

45

50

lol +↑

−↑ass

−↑shit

−↑damn

−↑gone

−↑bitch

−↑no

me +↑

−↑ain’t

−↑arrest

−↑hell

−↑don’t

like +↑

+↓haha

+↓new

−↑dont

+↓great

girl +↑

+↓park

−↑hate

−↑hurt

−↑boo

sleep +↑

+↓beach

+↓happy

−↑not

−↑stupid

money +↑

−↑bored

−↑down

+↓home

−↑pissed

+↓awesome

−↑ugh

−↑smoke

−↑mad

my +↑

funny +↑

mama +↑

−↑jail

−↑ugly

−↑nobody

+↓amazing

−↑bad

real +↑

−↑cant

+↓thanks

−↑hungry

+↓hahaha

−↑lied

Per word ave rage happ iness sh i f t δh av g, r (%)

Word

rankr

Tre f: Al l USA (havg=6.00)Tcomp: Beaumont, TX (havg=5.83)


+↓ +↑

−↑ −↓

Balan ce :

−182 : +82

−100 0

100

101

102

103

104

Pr

i=1δ havg, i

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A local reaction:

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Reactions:

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Another reaction:

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Blog post: Where is the happiest city in the US?

http://www.uvm.edu


http://compstorylab.com/2013/02/18/where-is-the-happiest-city-in-the-usa/


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Outline

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example. Of course, individuals observed to have a large radius couldbe tourists, or they could have a long commute. Nevertheless, we findno statistical evidence for this trend. Comparing individuals whoseaverage location falls in an area of small vs. large tweet density, weobserve little difference in their average gyradii (not shown).Moving beyond these four urban areas and looking at 472 cities in

the U.S., we do find a moderate correlation between the mean gyr-adius and city land area (Pearson r5 0.24, p5 23 1027); Fig. S3 andTable S4 show the top and bottom cities with respect to gyradii.To investigate the shape of human mobility, we normalize each

individual’s trajectory to a common reference frame (see Methods).In Fig. 3, we plot a heat map of the probability density function of thenormalized locations of all individuals. For the purposes of this dis-cussion, we will refer to deviations from an individual’s expectedlocation in the normalized reference frame as occurring in the direc-tions north, south, east, and west. Several features of the map revealinteresting patterns of movement. First, the overall west-to-east tear-drop shape of the contours demonstrates that people travel predo-minantly along their principle axis, namely heading west from theorigin along y/sy 5 0, with deviations in the orthogonal directionbecoming shorter and less frequent as they move farther away fromthe origin.Second, the appearance of two spatially distinct yellow regions

separated by a less populated green region suggests that people spendthe vast majority of their time near two locations. We refer to theselocations as the work and home locales8, where the home locale is

centered on the dark red region roughly 1 standard deviation east ofthe origin, and the work locale is centered approximately 2 standarddeviations west of the origin. These locations highlight the bimodaldistribution of principal axis corridor messages (Fig. 4A).Finally, a clear asymmetry is observed about the x/sx 5 0 axis

indicating the increasingly isotropic variation in movement sur-rounding the home locale, as compared to the work locale. We inter-pret this to be a reflection of the tendency to bemore familiar with thesurroundings of one’s home, and to explore these surroundings in amore social context (Fig. 4B). The symmetry observed when reflect-ing about the y/sy 5 0-axis is strong, demonstrating the remarkableconsistency of the movement patterns revealed by the data.In an effort to characterize the temporal and spatial structure

observed in Fig. 3, in Fig. 5 we examine locations frequently visitedby the most active members of our data set, namely the roughly 300individuals for whom we received at least 800 geolocated messages.We suspect that these individuals enabled the geolocating feature tobe on by default for all messages, as implied by the roughly O(104)geolocated messages suggested by the gardenhose rate. In Fig. 5, wefocus on these individuals specifically; of all participants, their pro-lific tweet activity most accurately reflects their movement profile.The main figure shows the probability of tweeting from each

locale, with locales ordered by rank, for each individual8. We find

that P Hað Þi

!R Hað Þi

1:3

which is approximately a Zipf distri-

bution29. This finding indicates that regardless of the number of tweet

Figure 2 | The gyradius, calculated for each individual, is shown for each tweet authored in four example cities. Tweet activity reflects population

density, with urban areas clearly visible in each city. Histograms of gyradii for each city are shown in Fig. S1, alongwith tweet locations colored by distance

from expected location (Fig. S2). The number of tweets shown for each city isN5 56650 (Chicago),N5 103,213 (Los Angeles),N5 42,089 (New York

City), and N 5 45,754 (San Francisco). Note that higher resolution versions of the four panels above can be found online28. Maps were created using

Matlab.

www.nature.com/scientificreports

SCIENTIFIC REPORTS | 3 : 2625 | DOI: 10.1038/srep02625 3

“Happiness and the Patterns of Life: AStudy of Geolocated Tweets”Frank, Mitchell, Dodds, Danforth, andDodds.Nature Scientific Reports, 3, 2625, 2013. [14]

Table 1 | Example language assessment by Mechanical Turk(labMT)27,30 words and scores. Words with neutral scores 4

word

Figure 6 | (A) Average happiness of words written as a function of distance from an author’s expected location, with tweets grouped into ten equally

populated bins. Expressed happiness grows logarithmically with distance distance from expected location. (B) A similar trend is observed when

individuals are grouped into ten equally populated bins according to their gyradius. Both trends persist through variations in binning and different

measures of mobility.

www.nature.com/

SCIENTIFIC

We grow fonder as we wander.

http://www.uvm.edu/pdodds/research/papers/others/everything/frank2013a.pdf




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Anger:word avg 𝜎war 4.16 1.01

torture 3.92 1.16murdered 3.84 1.14

motherfker 3.82 1.29anger 3.80 1.26killer 3.78 1.38fury 3.63 1.56

bombing 3.58 1.39⋮ ⋮ ⋮play 1.06 0.31idea 1.06 0.31

daughter-in-law 1.06 0.24piano 1.06 0.31stars 1.06 0.24tasty 1.04 0.20

thankful 1.02 0.14happy 1.00 0.00

Disgust:word avg 𝜎war 4.16 1.01

tortured 3.74 1.28whore 3.67 1.39

murdered 3.66 1.41asshole 3.56 1.28

killer 3.55 1.50motherfker 3.54 1.36

died 3.48 1.43holocaust 3.40 1.64⋮ ⋮ ⋮

hawaii 1.06 0.24arts 1.06 0.42joy 1.04 0.20

relaxing 1.04 0.28foundation 1.04 0.20

relax 1.04 0.20piano 1.04 0.28

presence 1.00 0.00

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Fear:word avg 𝜎war 4.20 1.02

tortured 4.18 1.39death 4.18 1.21killer 4.11 1.37

murdered 4.06 1.10jail 3.90 1.08⋮ ⋮ ⋮

banana 1.08 0.34right 1.08 0.34

properties 1.08 0.27cute 1.06 0.24topic 1.06 0.24active 1.06 0.24

wonderful 1.06 0.31dear 1.06 0.31bath 1.02 0.14

Surprise:word avg 𝜎

motherfker 3.93 1.35murdered 3.66 1.37bombing 3.52 1.49

death 3.50 1.51fatal 3.50 1.43

lottery 3.46 1.54torture 3.42 1.54

slap 3.41 1.49died 3.38 1.47

earthquake 3.32 1.54⋮ ⋮ ⋮flag 1.30 0.67

doors 1.30 0.64b/c 1.28 0.75

stuart 1.26 0.63pro 1.24 0.59

beans 1.24 0.59johnson 1.18 0.65

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angerdisgust

sadness

fear surprise

happiness

earthquake:

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grreww

wah

eek wow

ha!

earthquake:

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earthquake:

Images from Scott McCloud’s “Making Comics.” [27]

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laughter: war: rotting:

tortured: lottery: veterans:


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References I

[1] C. A. Bliss, I. M. Kloumann, K. D. Harris, C. M.Danforth, and P. S. Dodds.Twitter reciprocal reply networks exhibitassortativity with respect to happiness.Journal of Computational Science, 3:388–397,2012. pdf

[2] M. M. Bradley and P. J. Lang.Affective norms for english words (anew): Stimuli,instruction manual and affective ratings.Technical report c-1, University of Florida,Gainesville, FL, 1999.

[3] H. Chang.Inventing temperature: Measurement andscientific progress.Oxford University Press, 2004.

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http://www.uvm.edu/pdodds/research/papers/others/2012/bliss2012a.pdf


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References II

[4] E. M. Cody, A. J. Reagan, P. S. Dodds, and C. M.Danforth.Public opinion polling with Twitter, 2016.Available online athttps://arxiv.org/abs/1608.02024. pdf

[5] T. Conner Christensen, L. Feldman Barrett,E. Bliss-Moreau, K. Lebo, and C. Kaschub.A practical guide to experience-samplingprocedures.Journal of Happiness Studies, 4:53–78, 2003.

[6] M. Csikszentmihalyi.Flow.Harper & Row, New York, 1990.

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https://arxiv.org/abs/1608.02024

http://www.uvm.edu/pdodds/research/papers/others/2016/cody2016b.pdf


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References III

[7] M. Csikszentmihalyi, R. Larson, and S. Prescott.The ecology of adolescent activity andexperience.Journal of Youth and Adolescence, 6:281–294,1977.

[8] E. Diener and M. Y. Chan.Happy people live longer: Subjective well-beingcontributes to health and longevity.Applied Psychology: Health and Well-Being,3:1–43, 2011. pdf

[9] P. S. Dodds and C. M. Danforth.Measuring the happiness of large-scale writtenexpression: Songs, blogs, and presidents.Journal of Happiness Studies, 11(4):441–456,2009. pdf

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http://www.uvm.edu/pdodds/research/papers/others/2011/diener2011a.pdf

http://www.uvm.edu/pdodds/research/papers/others/2009/dodds2009c.pdf


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References IV

[10] P. S. Dodds and C. M. Danforth.Measuring the happiness of large-scale writtenexpression: songs, blogs, and presidents.Journal of Happiness Studies, 2009.doi:10.1007/s10902-009-9150-9. pdf

[11] P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A.Bliss, and C. M. Danforth.Temporal patterns of happiness and informationin a global social network: Hedonometrics andTwitter.PLoS ONE, 6:e26752, 2011. pdf

[12] R. A. Easterlin.Income and happiness: towards a unified theory.The Economic Journal, 111:465–484, 2001. pdf

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http://www.uvm.edu/pdodds/research/papers/others/2009/dodds2009b.pdf

http://www.uvm.edu/pdodds/research/papers/others/2011/dodds2011e.pdf

http://www.uvm.edu/pdodds/research/papers/others/2001/easterlin2001a.pdf


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References V

[13] R. A. Easterlin.Explaining happiness.Proc. Natl. Acad. Sci., 100:11176–11183, 2003.pdf

[14] M. R. Frank, L. Mitchell, P. S. Dodds, and C. M.Danforth.Happiness and the patterns of life: A study ofgeolocated Tweets.Nature Scientific Reports, 3:2625, 2013. pdf

[15] H. Gunaratana.Mindfulness in Plain English.Wisdom Publications, Somerville, Massachusetts,1992.

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http://www.uvm.edu/pdodds/research/papers/others/2003/easterlin2003a.pdf

http://www.uvm.edu/pdodds/research/papers/others/2013/frank2013a.pdf


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References VI

[16] E. Hoffer.The Passionate State of Mind: And OtherAphorisms.Buccaneer Books, 1954.

[17] A. R. Jensen.Bias in mental testing.1980.

[18] W. T. Jones.The Classical Mind.Harcourt, Brace, Jovanovich, New York, 1970.

[19] D. Kahneman, A. B. Krueger, D. A. Schkade,N. Schwarz, and A. A. Stone.A survey method for characterizing daily lifeexperience: The day reconstruction method.Science, 306(5702):1776–1780, 2004. pdf

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http://www.uvm.edu/pdodds/research/papers/others/2004/kahneman2004a.pdf


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References VII[20] K. Kennedy Townsend.

The Pursuit of Happiness: What the Foundersmeant—And didn’t.The Atlantic, 2011.http://www.theatlantic.com/business/archive/2011/06/the-pursuit-of-happiness-what-the-founders-meant-and-didnt/240708/.

[21] K. Kennedy Townsend.What makes life worthwhile? GDP won’t tell you.The Atlantic, 2011.http://www.theatlantic.com/business/archive/2011/06/what-makes-life-worthwhile-gdp-wont-tell-you/240343/.

[22] M. A. Killingsworth and D. T. Gilbert.A wondering mind is an unhappy mind.Science Magazine, 330:932, 2010. pdf

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http://www.theatlantic.com/business/archive/2011/06/the-pursuit-of-happiness-what-the-founders-meant-and-didnt/240708/



http://www.theatlantic.com/business/archive/2011/06/what-makes-life-worthwhile-gdp-wont-tell-you/240343/



http://www.uvm.edu/pdodds/research/papers/others/2010/killingsworth2010a.pdf


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References VIII

[23] I. M. Kloumann, C. M. Danforth, K. D. Harris, C. A.Bliss, and P. S. Dodds.Positivity of the English language.PLoS ONE, 7:e29484, 2012. pdf

[24] R. Layard.Happiness.The Penguin Press, London, 2005.

[25] S. Lyubomirsky.The How of Happiness.The Penguin Press, New York, 2007.

[26] C. Martinelli and S. W. Parker.Deception and misreporting in a social program.forthcoming in Journal of the European EconomicAssociation, 2007. pdf

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http://www.uvm.edu/pdodds/research/papers/others/2012/kloumann2012a.pdf

http://www.uvm.edu/pdodds/research/papers/others/2007/martinelli2007a.pdf


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References IX

[27] S. McCloud.Making Comics.William Morrow, 2006.

[28] G. Miller.Social Scientists wade into the Tweet stream.Science Magazine, 333:1814–1815, 2011. pdf

[29] L. Mitchell, M. R. Frank, K. D. Harris, P. S. Dodds,and C. M. Danforth.The geography of happiness: Connecting Twittersentiment and expression, demographics, andobjective characteristics of place.PLoS ONE, 8:e64417, 2013. pdf

[30] C. Osgood, G. Suci, and P. Tannenbaum.The Measurement of Meaning.University of Illinois, Urbana, IL, 1957.

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http://www.uvm.edu/pdodds/research/papers/others/2011/miller2011a.pdf

http://www.uvm.edu/pdodds/research/papers/others/2013/mitchell2013a.pdf


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References X

[31] A. J. Reagan, B. F. Tivnan, J. R. Williams, C. M.Danforth, and P. S. Dodds.Sentiment analysis methods for understandinglarge-scale texts: A case for usingcontinuum-scored words and word shift graphs.EPJ Data Science, 6, 2017. pdf

[32] E. Sandhaus.The New York Times Annotated Corpus.Linguistic Data Consortium, Philadelphia, 2008.

[33] D. Sobel.Longitude: The True Story of a Lone Genius WhoSolved the Greatest Scientific Problem of HisTime.Bloomsbury Publishing, US.

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http://www.uvm.edu/pdodds/research/papers/others/2017/reagan2017a.pdf

Measuring the Happiness, Health, and Stories of Populations

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