International Relations: State-Driven and Citizen-Driven Networks

Symposium Article

International Relations:State-Driven and Citizen-DrivenNetworks

Andrew Crooks1, David Masad1, Arie Croitoru2, Amy Cotnoir2,Anthony Stefanidis2, and Jacek Radzikowski2

AbstractThe international community can be viewed as a set of networks manifested through varioustransnational activities. The availability of longitudinal data sets such as international armstrades and United Nations General Assembly (UNGA) allows for the study of state-driveninteractions over time. In parallel to this top-down approach, the recent emergence of socialmedia is fostering a bottom-up and citizen-driven avenue for international relations (IRs). Thecomparison of these two network types offers a new lens to study the alignment betweenstates and their people. This article presents a network-driven approach to analyze commu-nities as they are established through different forms of bottom-up (e.g., Twitter) and top-down (e.g., UNGA voting records and international arms trade records) IRs. By constructingand comparing different network communities, we were able to evaluate the similaritiesbetween state-driven and citizen-driven networks. In order to validate our approach we iden-tified communities in UNGA voting records during and after the Cold War. Our approachshowed that the similarity between UNGA communities during and after the Cold War was0.55 and 0.81, respectively (in a 0–1 scale). To explore the state- versus citizen-driven interac-tions, we focused on the recent events in Syria within Twitter over a sample period of 1month. The analysis of these data show a clear misalignment (0.25) between citizen-formedinternational networks and the ones established by the Syrian government (e.g., through itsUNGA voting patterns).

Keywordssocial network analysis, international networks, international relations, social media

1 Department of Computtional Social Science, Krasnow Institute for Advanced Study, George Mason University, Fairfax,

VA, USA2 Department of Geography and Geoinformaiton Science, George Mason University, Fairfax, VA, USA

Corresponding Author:

Andrew Crooks, Department of Computtional Social Science, Krasnow Institute for Advanced Study, George Mason

University, 379 Research Hall, MX 6B2, Fairfax, VA 22030, USA.

Email: [email protected]

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Introduction

The international community can be viewed as a set of networks manifested through various trans-

national activities. Just as individuals interact both with their immediate neighbors and with distant

peers, whether by communication, trade, cooperation, or conflict, so do countries as well. Unlike

individuals though, state interactions occur at multiple levels of authority, introducing the classic

level-of-analysis issue in international relations (IRs; Singer, 1961). Such interactions range, for

example, from government policies and industry trade to transnational organization activities and

other community-forming functions (Maoz, 2012a). Each of these levels of interaction can be ana-

lyzed independently, identifying the networks of states that make it up. For example, a network may

be describing trade relations among states (Fagiolo, Reyes, & Schiavo, 2010), while another may be

describing immigration patterns (Poros, 2011).

Recent work on the adoption of a network approach to analyze IR has addressed a variety of inter-

esting issues, ranging from issues of power within such networks (Hafner-Burton, Kahler, & Mon-

tgomery, 2009) to the adaptation of classic network metrics and processes to describe IR events.

Notable examples of the latter include studies on the correlation between structural affinity and the

probability of dyadic conflicts (Maoz, 2010), and the description of the formation of alliance net-

works through homophily, and trade networks through preferential attachment (Maoz, 2012b).

Transnational interactions have been traditionally studied at the level of government activities

(Maoz, 2012a). Governments may sign treaties with each other or go to war, cooperate in interna-

tional institutions, trade directly with one another, and more. Even at this specific level, the networks

formed through such interactions may be very complex and multidimensional (Maoz, 2010). Coun-

tries may be joined in some form (e.g., via membership in an alliance such as North Atlantic Treaty

Organization), but they may be rivals in another venue (e.g., in their voting patterns at UN), or they

may not interact at all by some other measure (such as direct arms transfers). Thus, a comprehensive

view of the connections that shape IR requires analyzing not a single network but many at the same

time. In doing so, governments are establishing ties akin to the ones that connect individuals in a

social network, and social network theory provides an efficient mechanism to study these links.

The proliferation of social media is imposing a novel challenge to the traditional view of IRs as

the primary purview of government activities. Recent events, from the Arab Spring to the ongoing

prolonged Syrian civil war (Wolfsfeld, Segev, & Sheafer, 2013), have demonstrated vividly how the

general public is using social media to form their own connected communities, transcending estab-

lished administrative boundaries (Quercia, Capra, & Crowcroft, 2012; Takhteyev, Gruzd, & Well-

man, 2012). These communities bypass government censorship to break news directly to the broader

international community (Palmer & Nicey, 2012), raise civic engagement (Obar, Zube, & Lampe,

2012), and in general establish transnational connections (Stefanidis et al., 2013). To a certain

extend, one could refer to this as Cyber IRs , which complement traditional state-driven IR to shape

a global geopolitical landscape.

To be sure, international activism and communication are not new. Transnational advocacy net-

works have existed for many centuries. The Republic of Letters of the age of the enlightenment in

the 17th century Europe (Goodman, 1996) and the anti-slavery movement that stretched across the

Atlantic from North America to Great Britain (Keck & Sikkink, 1998) are just two notable historical

examples. Activists could communicate via letters and later by telegrams as well, spreading infor-

mation and coordinating action. However, the online communications formed around social media

interaction are different for several reasons. First, they are a deviation from the traditional one-to-

one (e.g., letters) and one-to-many (e.g., books and pamphlets) to a many-to-many communication.

Thus, they form communities rather than supporting the simple communication among members of

already established communities (e.g., Kwak, Lee, Park, & Moon, 2010). Furthermore, they are

rapid, with messages transmitted functionally instantaneously and information disseminated over

206 Social Science Computer Review 32(2)




the course of minutes and hours rather than weeks or months (Crooks, Croitoru, Stefanidis, & Rad-

zikowski, 2013).

Lastly, from the perspective of social science, social media may also be more readily studied.

Much of the communication, particularly via services such as Twitter, is open and accessibl, and can

be rapidly acquired, parsed, and analyzed with the help of computational techniques. Thus, the over-

all communication networks are more amenable to study than ever before. For example, earlier work

in this general direction of studying web-enabled interactions includes the analysis of instant messa-

ging networks (Leskovec & Horvitz, 2008) as well as the more recent study of transnational patterns

of e-mail communications (State, Park, Weber, Mejova, & Macy, 2013). In this article, we extend

this body of work by addressing international communities formed in social media around national

issues, using Twitter as representative example, and considering the ongoing civil unrest in Syria as

a test case. Community formation in Twitter represents a new, hybrid form of cyber IRs. It can be

viewed as a bottom-up (or grass roots) effort that complements the traditional top-down or

government-driven IRs. We proceed by analyzing both cyber and government-driven IR data using

a social network analysis (SNA) approach. Our objective is to pursue a new insight into the multi-

dimensional nature of these networks in order to provide a better lens to observe IR shaped as the

composite of networks formed and operating at different levels of governance and citizenship. In

addition to revealing transnational networks and their structure, the aggregate view of such networks

can provide valuable information into the alignment (or lack of, as is the case in our test case) among

the government and its people, which can be viewed as an indicator of political stability.

In order to present our argument, we use as an example Syria and data about its IR networks as

they are established through both top-down and bottom-up activities. For the former (top-down), we

use UN voting and arms trade data. For the latter, we use Twitter data that we harvested and analyzed

in order to assess the spatial distribution of communities formed there. Our objective is to demon-

strate how a SNA approach can reveal meaningful patterns and how the combined study of these

various networks provides a more holistic view of the problem. Our article is organized as follows.

In the second section, we provide a brief overview of IR studies utilizing SNA. In the third section,

we present our approach to study the top-down manifestations of IRs, while in the fourth section, we

present a comparable analysis of bottom-up efforts using Syria as a representative example, fifth sec-

tion provides a comparison of the two (top-down and bottom-up), and we conclude with a commen-

tary and outlook in the sixth section.

Background

While the world is comprised of distinct nation-states, the numerous connections among them, be

it through immigration, trade, and so on, have forced the relevant scientific communities to view

the global system as a system of networks (e.g., Agnew, 1999; Mueller, 2010). Indeed, early

approaches to study the world system through network analysis began in the late 70s, with the

work of Snyder and Kick (1979) who studied the formation of international networks via trade

flows, military interventions, diplomatic relations, and conjoint treaties. SNA, with a long history

in the social and mathematical sciences (Wasserman & Faust, 1994), is a natural choice to study

such complex systems. SNA provides a suitable lens to study the relationships among individuals,

groups, or organizations as they form complex systems; it allows us to explore how different parts

of a system are linked together and to define the overall structure of that system and its evolution

over time. Before we discuss how SNA tools have been used within our study (third and fourth

sections), in this section we review the state of network analysis as it relates to the studies of com-

plex transnational systems.

Network analysis has been shown to play a crucial role in various complex systems (Barabasi,

2012), ranging from the Internet (Dodge & Kitchin, 2001) to human interactions (Ratti et al.,

Crooks et al. 207




2010). Moving to the study of IRs, the analysis of voting patterns in the United Nations General

Assembly (UNGA) has been a major reference for such studies. Voeten (2000) showed the East–

West conflict between the United States and their allies on one side and the Soviet bloc on the

other. The UNGA bloc voting behavior was also studied before (e.g., Russett, 1966) and after the

Cold War (e.g., Holloway & Tomlinson, 1995), but these studies reflected the application of stan-

dard statistical techniques, rather than a network study. Addressing the same problem from a net-

work analysis perspective, recently Macon, Mucha, and Porter (2012) showed how the use of

community detection algorithms highlights the East–West split in voting during the Cold War,

a split that was substituted by a North–South split after the collapse of the Soviet Union. Earlier,

Hafner-Burton, Kahler, and Montgomery (2009) had provided a good overview of the emerging

potential of using network analysis to study IRs, while Fagiolo, Reyes, and Schiavo (2010) ana-

lyzed trade activities among states.

While the majority of IR studies had focused on the top-down view of the world, the prolifera-

tion of technological advancements (and the increased availability of relevant data sets) has

enabled the emergence of a bottom-up study of transnational interactions (State et al., 2013).

These studies in a sense build on network analysis capabilities that were developed in diverse

applications, such as epidemiology (e.g., Corley, Cook, Mikler, & Singh, 2010) or communica-

tions (e.g., Ratti et al., 2010), and apply similar analyses to transnational activities. As we men-

tioned earlier, State et al., (2013) studied the flows of e-mails between countries to identify

cross-national integration, while Leskovec and Horvitz (2008) mined instant messaging patterns

to show the strong links between colonial pasts, migration, and countries with close geographical

proximity. Gruzd, Wellman, and Takhteyev (2011) discussed community formation in social

media, while Stefanidis et al. (2013) studied the geographical visualization of such globally dis-

tributed communities.

In this publication, we contribute to the above-presented state of knowledge by presenting a SNA-

driven approach to analyze IRs holistically, as it is seen and pursued from the different perspectives

of governments and their people. In order to study this, we use the same approach of defining com-

munities and apply it to different data in order to compare the geopolitical orientations of a govern-

ment and its people.

Top-Down Networks Formed Through Government Activities

Government activities lead to the formation of international communities, linking nations into blocs

of allies. Identifying these blocs is comparable to community detection in SNA. The common objec-

tive of community detection algorithms is to identify groups of nodes that are significantly more

likely to have edges connecting them to other nodes in the community than they are to nodes outside

of it. In an extreme case, this would lead to the formation of cliques, whereby all nodes in a clique are

connected to each other. Fortunato (2010) provides a comprehensive review of community detection

algorithms. In this section, we present the application of community detection techniques in records

of government-led IR activities, in order to demonstrate their potential. We do so using two different

data sets: voting records of the UNGA (section on UNGA Voting Records) and data on international

arms trades (section on Arms Trade).

UNGA Voting Records

The UNGA currently comprises 193 member states, which meet in annual sessions, generally from

September to December and vote on resolutions put forward by other member states, with each

member having a single vote. When two countries are voting together in favor or against a





resolution, we may reasonably infer that they are ‘‘closer’’ to each other than to countries on the

other side of the resolution.

The web-accessible UNGA voting data set (Strezhnev & Voeten, 2013) provides a record of

each country’s vote on every resolution from the founding of the UN through April 2012. We use

this data to define affinity matrices, recording the similarity between pairs of countries during a

given period of time. Following the methodology of Macon et al. (2012), we remove all unani-

mous resolutions from the data set: they tend to be largely symbolic and they are not a useful mea-

sure of state affinity. We recode the data set to only record whether a country voted in favor of a

resolution or not; thus, we do not differentiate between a vote against a resolution and an absten-

tion. Simply put, the affinity between two states is the percentage of resolutions they voted

together on divided by the total number of resolutions. More formally, we define a function

drði; jÞ ¼ 1, when countries i, j vote the same way on a given resolution r out of total resolutions

R, and 0 otherwise. The affinity matrix A is then defined as an N � N matrix, s.t.:

A ¼a11 � � � a1n

..

. . .. ..

.

an1 � � � ann

264

375 ð1Þ

where: aij ¼ aji ¼PRr¼0

drði;jÞR:

This matrix can also be interpreted as the adjacency matrix of a graph: each country is then

a node and has edges connecting it to all other countries, with the affinity as the edge weight.

Treating the data as a graph, in turn, allows us to bring the tools of SNA to bear against it, in

particular community detection. As we are considering Syria as our test case, the communities

we are most interested in detecting are those emerging in the most recent resolutions (i.e., since

the outbreak of the Syrian civil war). In order to detect communities we use the Louvain

method for modularity community detection (Blondel, Guillaume, Lambiotte, & Lefebvre,

2008). The Louvain method is particularly suitable for our purposes for several reasons: it takes

into account the edge weights (since otherwise it would view the graph as one large commu-

nity) and is not initiated with a specific number of communities in mind. This second point is

important, since it means that we are not making assumptions about the number of communities

we are looking for as the communities emerge wholly algorithmically (i.e., we are not using a

predefined number of voting groups).

As a validation of this approach for our study, we first test this methodology against known com-

munities. We analyzed resolutions for the years 1950–1989, the period of the Cold War. The results

of our community detection are shown in the maps of Figure 1, where states with the same color are

considered parts of the same community by the algorithm. It is easy to see that the results are mean-

ingful: the algorithm has correctly sorted the Western bloc from the Eastern bloc in the period 1950–

1989, and even captured China’s sphere of influence in sub-Saharan Africa. It also captured per-

fectly the realignment of geopolitical blocs following the fall of the Eastern bloc (1990–2000 and

later) and the emergence of a North–South split. Interestingly, Russia is now part of the same cluster

as the United States and Western Europe. One can also note that Syria, unsurprisingly, falls in the

southern cluster, as does the rest of the Arab world and the Gulf states. Turkey and Israel, in contrast,

are both part of the Northern bloc.

We further divided the UNGA data into votes before and after the beginning of the Arab Spring and

studied whether we can observe any changes in worldwide alignment. In fact, the cluster membership

of the Middle East is stable. However, this does not tell the entire story. If we look closer at the affinity

between Syria and some of its regional neighbors, we do see steep declines as shown in Table 1. In this

case, we define affinity as a percentage of accordance in roll call votes (excluding unanimous votes).

Crooks et al. 209




For example, Table 1 shows the 10 countries with the highest affinity with Syria in the pre–

Arab Spring period, with all UN member states’ average given for comparison. Noticeably, sev-

eral of these (marked with a *) are countries whose governments were overthrown over the course

Figure 1. Voting blocs in the United Nations General Assembly (UNGA): Countries depicted in the samecolor tended to vote together.





of the Arab Spring; these countries also show a sharp, above-average decline in affinity between

the two periods. However, their affinity is still higher than the average, and the decline in the affi-

nity is smaller than some (Canada is a striking example, showing a decline from 41.6% to 14.8%).

This suggests that, while the countries are sharply diverging, they are still closer to each other than

they are to many others. This appears to be the reason that we do not see change in the worldwide

cluster membership.

Arms Trade

We analyzed in the same manner arms transfer data to obtain another indicator of government-led IR net-

working. A state is more likely to transfer arms to its friends than to its enemies; thus, a transfer of arms

from one state to another is a strong sign of alliance or cooperation. The Stockholm International Peace

Research Institute (SIPRI) maintains a web-accessible database of major, conventional arms transfers

dating from 1950 measured in a normalized measure of value (SIPRI, 2012).1 Arms transfers can also

be viewed as a network, this time a directed one, with an edge representing a transfer from state to state.

We use this data to construct a network, where edge weights are the sum of transfers during a given period.

As we see in Figure 2 (top), the Cold War period offers ample validation, capturing the

Cold War partition of the geopolitical landscape. However, unlike the UN voting data, as

we move through the 1990s with the fall of communism, a greater number of communities

arise (Figure 2, middle). This is due to two factors, the first being that the UN data focus

on human rights, sovereignty, and so on, while the arms data focus more on security and not

necessarily politics. For instance, the United States and Saudi Arabia are not in the same com-

munities with respect to the UN voting patterns, but they are in the same community with

respect to arms transfers due to security issues. The 21st century brought a proliferation of

communities; in addition to the US- and Russia-dominated partitions, we see one that appears

centered on France, and another arising from the exports of Germany and the Scandinavian

countries. These patterns are consistent with the prevalent view of arms trades in security stud-

ies (Cooper & Mutimer, 2011).

When focusing on the Middle East, we see a fragmentation (Figure 2, bottom) that suggests a

more nuanced partitioning of the Middle East than the UN data and may help us understand why

the geopolitics of the Syrian conflict are playing out the way they are. Syria itself is part of the Rus-

sian cluster, while its neighbors Turkey, Iraq, and Israel are all part of the American cluster, and

Table 1. Countries With the Highest Affinity With Syria in the Pre-Arab Spring Period.

Country NameAffinity With

Syria 2001–2010Affinity With

Syria 2011–2012AffinityChange

Libya* 94.8% 77.0% –17.7%Egypt* 94.6% 86.9% –7.7%Sudan 94.3% 85.2% –9.1%Indonesia 94.3% 86.9% –7.4%Oman 94.3% 88.5% –7.4%Yemen* 94.3% 91.8% –5.8%Venezuela 94.3% 83.6% –2.5%United Arab Emirates 94.0% 83.6% –10.4%Tunisia* 93.8% 78.7% –15.2%Qatar 93.8% 86.9% –7.0%All state avg. 70.1% 65.5% –5.1%

* Countries whose governments were overthrown over the course of the Arab Spring.

Crooks et al. 211




Jordan is part of the Northern Europe cluster. Saudi Arabia and Qatar are also in the U.S. cluster,

while the United Arab Emirates (UAE) is in the French one. Iran, Syria’s primary patron, is part

of the same Russian cluster as Syria.

Figure 2. The Stockholm International Peace Research Institute (SIPRI) data showing arms trade blocs.Countries depicted in the same color are part of the same arms trade bloc.





Social Media Content Revealing a Bottom-Up View

The proliferation of social media has fostered the emergence of an alternate avenue to IRs, one that is

bottom-up, driven by individual citizens who form networks that cross national boundaries, to com-

municate their message to the international community. Social media played an important role in the

Arab Spring and continues being important in ongoing unrests in Syria and Turkey. Accordingly, we

studied international communities formed around the issue of Syria in social media, focusing in par-

ticular on Twitter. The Twitter community uses a variety of actions to establish connections among

its members, including following, mentioning, or retweeting.

Using our GeoSocial Gauge system (Croitoru, Crooks, Radzikowski, & Stefanidis, 2013), we har-

vested tweets referring to Syria from Twitter’s streaming application programming interface. Here

we present data collected over a monthlong period in the summer of 2012 (more specifically

between July 30 and August 30, 2012), which corresponds to the peak escalation of the Syrian con-

flict. This resulted in a data set of 3,184,136 tweets, each including the user name, the tweet’s time

stamp, and the text of the tweet itself. From among them, 1,316,120 were retweets. The information

also included the user’s reported location. For a small subset, this location is reported as precise lati-

tude and longitude coordinates inserted by the Twitter software via a Global Positioning System

(GPS) receiver (0.72% of our sample), yet a much larger subset of tweets has location information

in the form of a toponym describing the user’s location. This can be provided either by the user

directly or by the application itself (geotagging a user’s location based on his or her Internet Protocol

(IP) address). In our data set, 43.39% of all tweets carried geolocation information in the form of a

toponym. In the retweet subset of our data set, the percentage of geolocated entries was 40.40%. The

geographical distribution of our tweet data corpus is shown in Figure 3.

We analyzed information flow in this data sets by focusing on retweets: tweets initially composed

by one user and repeated by others to their followers. We also studied mentions: users directly refer-

ring to other users. Retweeting and mentioning are particularly interesting as they indicate an active

connection compared to following, which is rather passive. Nevertheless, we should mention here

that the following analysis can also be applied to follower data sets in Twitter. Similarly to the anal-

ysis presented in the third section, we followed an SNA approach to detect communities in our Twit-

ter data corpus, especially focusing on transnational links among users, and Figure 4 summarizes our

findings. As noted above, the number of distinct communities is not predefined but rather emerges

algorithmically from the data itself. We see that there is a higher fragmentation of the world in this

data set, compared to the results presented in the third section. This may suggest that citizens are

unconstrained by long established geopolitical alliances when they form their own communities

around a specific topic.

Nevertheless, this process does exhibit some structure, as these communities tend to be formed

around established sociocultural affinities. For example, we can observe that the majority of the 21

states of the Arab league are identified to be part of the same mention community (Figure 4, top). In

addition to validating our approach, this result also supports the notion of supra-nationalism (Cul-

casi, 2011) within the Arab world. Within the retweet network, we see that the Arab league countries

are broken into three district clusters with Egypt, Lebanon, Mauritania, Saudi Arabia, and Sudan in

Cluster 4. Algeria, Bahrain, Iraq, Jordan, Lybia, Morocco, Palestine, Tunisia, and Yemen in Cluster

6 and Comoros, Djibouti, Kuwait, Oman, Qatar, Somalia, and United Arab Emirates in Cluster 7

Retweet and mention activities establish links among states to generate international commu-

nities through citizen activities rather than state driven. In Figure 5, we show a map of the most pro-

minent transnational retweets aggregated to the state (i.e., tweets from one state retweeted from

another). We observe that even though Syria is the topic, the United States is acting as the central

node for this network and the United Kingdom, UAE, and Qatar are joining Syria in defining

and transmitting the message from the ground. This is consistent with traditional theories of

Crooks et al. 213




Figure 3. Distribution of tweets during our study period. Each green dot may correspond to multiple tweetsoriginating from that location.

Figure 4. Community detection of mention (top) and retweet (bottom) networks.





transnational activism (Keck & Sikkink, 1998), which describe activists ‘‘on the ground’’ collecting

information and disseminating it to allies outside their country.

Comparison of Top-Down and Bottom-Up Communities

Based on the different communities identified in the third and fourth sections, our analysis now turns

to measuring the similarity between the state- and citizen-driven networks. Given two communities

A and B, the similarity between them can be calculated by utilizing the Jaccard similarity coeffi-

cient, J(A, B), which is computed as:

J A;Bð Þ ¼ A\Bj jA[Bj j ð2Þ

where A\B is the intersection between A and B, A[B is the union of A and B, and | | is the set

cardinality. Using the community sets identified earlier in each network, we computed the similarity

between all possible pairs of communities using the Jaccard coefficient and selected the maximum

value. These values are summarized in Table 2.

What emerges from Table 2 is that our approach clearly identifies the evolution in the geopoli-

tical landscape between the Cold War era and the following periods. More specifically, the similarity

between UNGA communities during and after the Cold War was only 0.55 (in a 0–1 scale), showing

the effects of the dissolution of the Eastern bloc and the realignment of new international partner-

ships. This emerging landscape appears to be stabilizing, as the similarity between UNGA 1990s and

2000s increased to 0.81 on the same scale. Furthermore, we observe the greatest similarities between

communities drawn from the same networks (e.g., the UN) for different periods (e.g., nineties and

Cold War); however, more importantly, we see relatively stable overlap between communities from

different networks (e.g., the SIPRI Cold War communities and the UN communities during the Cold

War, the nineties, and the aughts). This may not be particularly surprising for the UNGA and SIPRI

networks, since they are both top-down, describing the actions of national governments.

What is more interesting is that the overlap exhibited between the Twitter communities and the

political ones. The bottom-up communities formed in Twitter show some similarity to the UNGA

and SIPRI communities, with the average being only 0.27, notably lower than the top-down

Figure 5. Transnational links established through retweets.

Crooks et al. 215




SIPRI-to-UNGA average of 0.42. This therefore suggests a misalignment between the Syrian gov-

ernment top-down and the citizen-formed bottom-up IR in these networks.

Discussion and Outlook

The emergence of social media is redefining the nature of the relationship between governments and

their peoples. On one hand, they support a communication revolution that can connect citizens in an

unofficial democracy (Yang, 2009). On the other, totalitarian regimes, aware of the power of this

emerging medium, either use its content to identify and persecute dissidents (Burns & Eltham,

2009) or attempt to alter the message conveyed through them, spinning it to their advantage (Mor-

ozov, 2009).

In this article, we are focused on the emerging role of social media as a new channel for empow-

ering citizens to engage in IRs and potentially influence the geopolitical landscape at the local,

regional, and even the global scale. Together with the growing interest in SNA, the rapid growth

of social media and longitudinal data sets are providing us new opportunities to develop new per-

spectives on IR. Through social media interactions, communities are being formed around topics

of discussion, or in response to events, that span across traditional state boundaries. These commu-

nities can be viewed as a citizen-driven form of IR and can be studied as we have shown here in the

same manner as that of state-driven IR. Furthermore, the comparison of these two networks (state

and citizen driven) can offer us a new lens for studying social and political realities in near real time.

In this work, we referred to this as Cyber IRs, which complement traditional state-driven IRs.

The UNGA and arms trade networks offer traditional views of the international state-to-state IR

networks. Such networks are driven by state IR institutes (e.g., the State Department) and are char-

acterized by the establishment of longstanding networks that are slow to respond to local short-term

events (particularly once aggregated) but offer long-term coverage. This level of temporal resolution

is useful for understanding slowly evolving large-scale phenomena, such as the Cold War, but often

falls short of capturing localized and highly dynamic international phenomena such as the response

to the Arab Spring.

In contrast, social media—and in particular Twitter data—offers extremely fine temporal resolu-

tion. While in this study we aggregated individual Twitter users into countries, the data shows a dif-

ferent set of clusters from that of state-driven networks, suggesting that what we observe within

Twitter is indeed a conversation dominated by voices both inside and outside of Syria. Furthermore,

the dynamic nature of social media allows for breaking news of events related to IRs to be broad-

casted to large audiences around the world. For example, during our study period, major peaks in

traffic and conversation related to the massacre in Aleppo and the kidnappings in Lebanon—all

Table 2. Similarity Scores Between Communities.

UN ColdWar

UNNineties

UNAughts

SIPRI ColdWar

SIPRINineties

SIPRIAughts

TwitterMentions

TwitterRetweets

UN Cold War 1.00 0.55 0.54 0.43 0.38 0.28 0.25 0.25UN nineties 0.55 1.00 0.81 0.48 0.30 0.33 0.32 0.26UN aughts 0.54 0.81 1.00 0.48 0.33 0.36 0.28 0.28SIPRI Cold War 0.43 0.48 0.48 1.00 0.35 0.27 0.27 0.29SIPRI nineties 0.38 0.30 0.33 0.35 1.00 0.47 0.30 0.22SIPRI aughts 0.28 0.33 0.36 0.27 0.47 1.00 0.27 0.24Twitter mentions 0.25 0.32 0.28 0.27 0.30 0.27 1.00 0.28Twitter retweets 0.25 0.26 0.28 0.29 0.22 0.24 0.28 1.00

Note. UN ¼ United Nations; SIPRI ¼ Stockholm International Peace Research Institute.





of which were broadcasted through social media and received considerable attention within the

international community. More importantly, the data from Twitter is capable at highlighting some

of the most salient countries: it is unlikely to be a coincidence that the countries with the highest

volumes of tweets are those that appear to be most closely involved in the conflict. Many countries

with a large Twitter users base (Lunden, 2012) barely appear in our data set and do not seem to be

driving the conversation. Certainly, volume of tweets is a strong indicator of the level of attention

paid to an issue by the residents or citizens of different countries. However, the absence of tweets

may also reveal important information: for example, Russia, which has a relatively large population

of Twitter users, does not appear significantly in our data set; notably, the Russian governments has

sided with the Syrian government and supported it in the UN Security Council.

Here we have looked at long-term trends in IRs and contrasted these top-down trends to more bottom-

up IR over a short period of time which has only become possible through the development of new

technologies and social media. Specifically what made this study possible is the rise in availability of

location information in social media services, a trend that is expected to increase. Empowered by such

information, our study moves us away from a world systems view of the world to a much more decen-

tralized view. The three networks studied here provide different views of international interactions, with

a particular focus on the Syrian conflict. Analyzing each network yielded distinct results that allowed us

to attempt to answer various questions. The traditional state-to-state networks provided powerful per-

spectives on the international system as a whole but are weaker when it came to understanding a specific,

contemporary crisis such as Syria. The Twitter data, in contrast, was up to date and extremely fine-

grained but only offered a limited view into the attention and conversation of individuals with each other.

Taken together, these networks offer a more holistic view of the entire situation than any network indi-

vidually and are a useful supplement for more traditional qualitative methods. By linking places and

communities across the globe and comparing them to state-driven interactions pave a way for new

metrics of government–people affinities, which could be used as indicators, for example, to civil unrest.

As discussed above, it will be particularly interesting to see whether any causality can be traced between

the actions of governments and the discussions of their people. To what extent are citizens influencing

their governments to act and to what extent are those government actions influencing the discussions of

their citizens? This article suggests that there is at least a connection between the two, which is visible in

the network data, and additional research is required to fully understand it.

By comparing and contrasting information gleaned from both state- and citizen-driven networks,

we are able to evaluate to what degree these two realms of IRs are aligned. In particular, our analysis

suggested a misalignment between state-driven IR, as manifested through UNGA voting and arms

trade, and citizen-driven IRs, as expressed in Twitter. Our objective in this article was to use this

particular case study in order to validate our methodology, rather than discovering unknown pat-

terns. By applying our methodology to this well-known situation we are able to demonstrate that this

approach is able to correctly detect communities both in state- and citizen-driven IR activities.

We believe that these results serve to indicate the potential of using social media for further

studying Cyber IRs and for further developing analysis tools specifically geared toward studying the

relations between Cyber IRs and state IRs. For example, it would be interesting to see what specific

topics are being discussed in various countries and relate these to UNGA voting patterns (along with

reactions to them or if states react to events in social media) or explore the sentiment of the messages

and how these relate to political events (e.g., Mejova, Srinivasan, & Boynton, 2013). Currently,

social media does not offer a longitudinal range comparable to more traditional IR data sets (as it

is relatively new by comparison to say the UNGA), however, given the current growth trends of

social media, this is expected to change as social media is becoming available to more potential users

around the world. While we have focused here on one social media platform (Twitter), it would also

be interesting to explore how different forms of social media (e.g., Facebook or other weblogs)

affect the formation and evolution of communities involved in IRs.

Crooks et al. 217




Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or

publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Note

1. However, it needs to be noted that not all arms transfers are between states, and illicit arms transfers by

private dealers is also possible but we consider this beyond the scope of this study.

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Authors Biographies

Andrew Crooks ([email protected]) is an assistant professor in the Department of Computational Social Sci-

ence (CSS) at George Mason University (GMU). His research work is in social complexity, GIS and agent-

based modeling (see www.gisagents.org).

David Masad ([email protected]) is a Ph.D. student in CSS at GMU. His research involves apply-

ing agent-based models and big data tools to policy questions.

Arie Croitoru ([email protected].) is an assistant professor in Department Geography and GeoInformation

Science (GGS) at GMU. His research revolves around geoinformatics.

Amy Cotnoir ([email protected]) is a Ph.D. candidate in the GGS at GMU. Her research focuses

on the using social media to understand geographical problems.

Anthony Stefanidis ([email protected]) is a professor in GGS and Director of the Center for Geospatial Intel-

ligence at GMU. His research relates to the analysis of spatiotemporal information and harvesting of geospatial

information (see www.astefanidis.org).

Jacek Radzikowski ([email protected]) is a Ph.D. student in GGS at GMU. His research focuses on collect-

ing and analyzing geosocial multi-media.





International Relations: State-Driven and Citizen-Driven Networks

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