BA POLITICS DISSERTATION (1)

The Empirical Factors of Twitter Adoption by World Governments Author: Andrea Pavón-Guinea ([email protected]) Supervisor: Daina Chiba ([email protected]) Date: 22/04/2015

UP:22/04/2015-07:00:16 WM:22/04/2015-07:00:18 M:GV831-6-FY A:14a2 R:1204747 C:FCFAFCFCA0F8901AE089734815B7D484BF104B78

| GV831 -‐ PROJECT POLITICS 2



THE EMPIRICAL FACTORS OF TWITTER ADOPTION BY WORLD GOVERNMENTS ANDREA PAVÓN-GUINEA1 Department of Government University of Essex

Although 86% of the countries in the world have already activated at least one Twitter account, the empirical determinants of Twitter adoption by world governments have been largely ignored in the study of politics and communication. Apart from a handful of publications that focus locally on the usage of Twitter by the American Congress Members, there is no other publication that has so far addressed globally the empirical factors that influence the likelihood of Twitter adoption by world leaders. I agree with Gainous and Wagner (2013: 2) that “as the use of social media becomes ubiquitous, measures of the impact of the new medium and testable theories of its importance are becoming vital to understanding this new political environment”. The truth is that despite the obvious significance of social media’s role in politics, research on the relationship between Twitter and governments is still scarce and underdeveloped: both the novelty of social media and the highly complex interdisciplinary nature of research on media and politics may have inhabited progress in the field. In this sense, this paper2 is designed to make a modest contribution toward the understanding of what political and socio-demographic factors influence governments’ adoption of Twitter. My primary interest is whether democratic countries are more likely to activate Twitter accounts. In order to assess this, I use binary logistic regression to estimate the effect of democracy on the likelihood of adopting Twitter. I also include several political, socio-demographic and time control variables. The unit of analysis in this study is coded in a country-month format and the observation period ranges from January 2006 to December 2012. The model3 indeed shows that democracies are more likely to activate Twitter accounts than other type of regimes. This may be due to the role that communication plays in a democratic regime; communication is indispensable both in the deliberation decision-making process and in the responsiveness usually associated with democratic governments. Also, communication is a coordination good usually exploited by democratic politicians in order to ensure political survival. Finally, in order to assess the robustness of the democracy variable, I have also calculated its substantive effects on the likelihood of Twitter adoption. Keywords: Twitter; politics; communication; democracy; logistic regression; time

1 The author would like to thank Daina Chiba for not only supervising this project, but also for being always readily available and for his invaluable insights into this study 2 Dataset is available upon request 3 All models and graphs were estimated using RStudio Version 0.98.1091 for Intel Mac OS X 10_10_2



LITERATURE REVIEW: TWITTER & POLITICS The relationship between Twitter and Politics has been studied by two main disciplines: communication research and political science. Regarding the former, researchers have focused on processes of agenda building and agenda setting. For instance, Parmelee (2013) found that tweets from political leaders influence how journalists decide to cover an issue. Also, Wallsten (2014) explained that although reporters are increasingly incorporating tweets into their news dissemination, the amount of “Twitterers” included usually belongs to the high ranks of national political leaders. Studies within communication research have also focused on integrating Twitter’s effects in the context of larger theoretical discussions. For instance, following the theory of selective exposure, people usually use media to find information in accordance with their political views (Jungherr, 2014). In this sense, Parmelee and Bichard (2012) discuss the political influence of tweets on followers of political actors: they conclude that users often follow politicians with whom they possess some kind of ideological affinity. In this respect, Conover et al (2011), Hanna et al (2013) and Smith et al (2014), addressed the phenomenon of political polarization on Twitter. They explored whether people gather into polarized crowds on Twitter, and demonstrated that, in fact, political retweets present a segregated partisan structure, where left and right-wing users hardly communicate. The political uses of Twitter have also been addressed by political scientists. For instance, whether the Internet fosters or hinders political discourses has been widely discussed (Chadwick, 2006; Neuman, Bimber and Hindman, 2011). Regarding Twitter, Kim and Park (2012) observe that non-mainstream, resource-deficient politicians are more likely to use Twitter as a means of political participation. Neuman (1991) has also analysed the impact of the new electronic media on mass audiences, concluding that it will not lead to its fragmentation. In this context, Davis (1999) also explains how the Internet is not intended to upset the traditional configuration of power: the actors that dominate the political sphere are also destined to dominate the microblogging sphere. On the other hand, some researchers expect Twitter to foster political engagement by creating networks among people. In this sense, Gainous and Wagner (2014) illustrate how these new online communities constitute forums where people exchange information outside the traditional media. This idea is closely related to Putnam’s theory of social capital (Jungherr, 2014).



Social media, in general, and Twitter, in particular, has also played a notable role in many recent political events. Lynch (2011) claims that the drastic change in the information environment over the last decade has led to a change in the ability to organize for collective action, and the transmission of information. In this respect, Morozov (2011) points out that both revolutionaries and authoritarian governments can use the Internet as a tool for political communication. Social media, thus, has been used to promote democracy, but also to entrench dictators and threaten dissidents. Moreover, social media, and Twitter especially, has been utilised in contentious politics. “The revolution will be Twittered!”, declared journalist Andrew Sullivan after protests erupted in Iran in June 2009. Furthermore, Cohen (2012) dubbed the 2012 Gaza Conflict as the first “Twitter War”, due to the extensive use of social media by Israel and Hamas. On this subject, Zeitzoff (2014) is examining in a working paper how international public’s support, via social media, shapes the strategies of the participants in the conflict. He has found that shifts in public support reduce conflict intensity, particularly for Israel. Conversely, increases in the attention of the international actors, such as Egypt, the United Nations or the United States, slightly increase the conflict intensity of both Hamas and Israel. All the studies mentioned utilise different methods in order to examine the political uses of Twitter: be it social network analysis (Kim and Park, 2012), surveys (Smith and Rainie, 2010), controlled experiments (Lee and Oh, 2012), or trace data, by which researchers collect data from applications such as Twitter’s API in order to document the activities of Twitter’s users (Ausserhofer and Maireder, 2013). The discussion about the political uses of Twitter has also been broadened by the use of qualitative methods, such as case studies (Chadwick, 2013), interviews (Parmelee, 2013) and content analysis of Twitter’s messages (Jackson and Likeller, 2011). More generally, research on the political uses of Twitter can be categorized into two broad topical categories: the use of Twitter by the public (for instance, Parmelee and Bichard, 2012; Hanna et al, 2013) and the use of Twitter by politicians. This paper is focused on the latter, which has been approached by various studies. For instance, Jackson and Lilleker (2011) concluded that just a small group of MPs use Twitter as a regular communication channel. On the other hand, Lee (2013) analysed the effects of politicians’ messages on their followers. She specifically conducted an experiment in order to investigate whether individuals respond differently to politicians’ Twitter messages and their TV interviews. She demonstrated that exposure to TV interviews (versus exposure to Twitter pages) heightened social presence, triggered more favourable evaluations and prompted less source-related thoughts.



Particularly, Burson-Marsteller, a global public relations and communications firm, conducted a global study of world leaders on Twitter. They identified 643 Twitter accounts of heads of state and government, foreign ministers and their institutions in 161 countries worldwide. Exactly 3,100 embassies and ambassadors are now active on Twitter. The study analyses each leader’s Twitter profiles and their connection with each other. As of 25 June 2014, 83% of the 193 UN member countries have a presence on Twitter, and 68% of all heads of state have personal accounts on the microblogging site. @BarackObama is the leader the most followed on Twitter: he tops the list with 43.7 million followers, followed by Pope Francis (@Pontifex) with 14 million followers and Indonesia’s President Susilo Bambang Yudhoyono (@SBYudhoyono), who has more than five million followers. Kenya’s Uhuru Kenyatta (@UKenyatta) is Africa’s most followed president with 457,307 followers and Turkey’s @Ahmet_Davutoglu is the most followed foreign minister with 1,511,772 followers. Perhaps surprisingly, the five most followed world leaders utilise Twitter as one-way communication tool: they are only following a handful of other world leaders, if any, and are hardly conversational. For instance, the @BarackObama account is a campaign account tailored for an American audience and almost never tweets about foreign affairs. However, there are some exceptions. The Swedish Foreign Ministry (@Swe_MFA) has unilaterally followed 355 other world leaders and the French Foreign Minister @LaurentFabius is following 250. Moreover, contrarily to the vast majority of accounts on Twitter, African leaders solely use Twitter to engage with their followers. Ugandan Prime Minister @AmamaMbabazi is the most conversational world leader: 95% of his tweets are replies to other Twitter users. Rwanda’s President @PaulKagame usually exchanges information with his critics. It is also worth noting the use of hashtags by world leaders in order to promote certain issues, be it #GiveMeFive to push for the release of five Cuban intelligence officers convicted in Miami of conspiracy to commit espionage in the US, or broader issues such as #ENDViolence against children (Burson-Marsteller, 2014). Finally, there is an evident lack of research on one specific topic: the influence of various variables on the likelihood of a politician adopting Twitter. In fact, the studies available solely concentrate on the United States. For instance, Williams and Gulati (2010) analysed which factors drive members of Congress to adopt Twitter. They found that being part of the Republican Party, campaign resources and a large vote share in the last elections are drivers leading to extensive usage of Twitter; however, urban constituency and age do not show a significant relationship with the likelihood of Twitter adoption. Also on this topic, Lassen and Brown (2010) reported that members of Congress are more likely to adopt Twitter if their party’s leaders urge them to, if they are young or if they serve in the Senate.



Third, Peterson (2012) examines Twitter’s use in the 111th Congress of the US, in order to address what variables shape the decision to adopt Twitter as part of the Congress’ media strategy. Using data collected from Congressmen Twitter accounts during the 2008 congressional election, he found that partisan, cohort and ideological determinants influence Twitter’s adoption: Republicans and ideological extremist parties are more likely to use Twitter. More globally, Barbera and Zeitzoff (2014) are working on a paper that looks into electoral timing and democracy as predictors of social media adoption. However, no other publication has so far addressed the empirical determinants of Twitter adoption by world governments, and that constitutes the main motivation of the present study.

MOTIVATION Despite the fact that research on the topic between social media and international relations is scarce and underdeveloped, I would argue that the significance of the media’s political role in contemporary and future diplomacy is enormous and therefore deserves close attention. The history of the Internet as a political medium is not a long one: the 1992 Clinton/Gore presidential election in the United States was the first major national campaign to make substantial use of the Internet: they made use of email in order to disseminate information on the campaign and position papers (Gainous and Wagner, 2009: 503). In fact, the first online political campaign took place as recently as in 2008, where Barack Obama and his staff used social media on a wide scale in order to target potential voters: “Tools and methods used in the American campaigns were immediately imitated in other parts of the world” (Gainous and Wagner, 2013: 17). Theoretically, Gilboa (2000) has proposed three conceptual models based on the degree to which diplomatic negotiations are exposed to the media and public opinion. First, he explains how secret diplomacy is characterized by the total exclusion of the media, and thus, the public, from negotiations: “Classic traditional diplomacy was secret, but as the world has become more democratic and accessible to the media, it has become more difficult to conduct diplomacy in secrecy” (Gilboa, 2000: 278). However, secrecy cannot always be assured when negotiations last for an extended period of time. In this sense, closed-door diplomacy refers to the method of excluding the most substantive aspects of the negotiating process, whilst allowing the media to publish the technical aspects of it, such as the date, place and persons involved the negotiation (Gilboa, 2000: 282).



Nevertheless, the heightened transparency of the information age has perhaps irreversibly damaged this secrecy (Kalathil, 2013). That is why Gilboa (2000) proposes a third model of open diplomacy, where the information regarding the negotiations is readily accessible to the media and to public scrutiny and debate: “Officials typically allow openness when they discuss relatively congenial issues such as culture, education, telecommunications, tourism, environment or health; conversely, they prefer the secret or the closed-door models to resolve political, strategic, security and trade issues” (Gilboa, 2000: 287). Practically, politicians are using Twitter in different ways. Golbeck et al (2010) analysed the content of over 6,000 Tweets from all members of the American Congress in order to conclude that they are primarily using Twitter to disseminate information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. Also on this subject, Bruns and Wilson (2010) explain that it is possible to identify three styles of social media use by governments. The first group are “managers”, whose presence on Twitter is controlled by other than the politician himself. In this regard, Twitter is treated as another “top-down, one-to-many channel” (Bruns and Wilson, 2010: 342). This is the approach most widely taken by politicians across the world. For instance, all of the G20 governments, but one, have an official Twitter presence, yet few world leaders are actually tweeting themselves. Notable exceptions include the Estonian President Toomas Henrik Ilves (@IlvesToomas) and Finnish Primer Minister (@AlexStubb) (Burson-Marsteller, 2014). The second group, dubbed as “e-democrats”, is composed by politicians that advertise their political activities and messages on Twitter, and also take the time to engage with the online citizenry: “They are thus making use of some of the affordances of social media and responding to the unwritten rules of such environments” (Bruns and Wilson, 2010: 342). Below I include some examples:

Figure 1. Tweet from Narendra Modi, Prime Minister of India



Figure 2. Tweet from David Cameron, Prime Minister of the United Kingdom

Figure 3. Tweet from Tony Abbott, Prime Minister of Australia

Finally, a third group of politicians are using Twitter in order to expose details of their personal lives: they are personally involved in the online community and recognise that social media should not be a top-down means to broadcast exclusively political messages (Bruns and Wilson, 2010: 342). Examples include:

Figure 4. Tweet from Alexander Stubb, Prime Minister of Finland



Figure 5. Conversational Tweet from Toomas Hendrik Ilves, President of Estonia

Perhaps, “the best way to conceptualize these changes is to think of ourselves as having departed the Age of Secrecy and entered the very beginning of a new epoch: the Age of Sharing” (Faris, 2013: 35). In this respect, it has been widely proclaimed that technology would transform world politics. Keohane and Nye (1998) explain that the amount of information transmitted among societies is now practically infinite due to the virtual disappearance of communication’s costs as a result of the information revolution. In fact, the world’s networked population has grown from the low millions to the low billions since the rise of the Internet in the early 1990s. Particularly, social media has been able to connect many actors worldwide, from regular citizens, to activists and governments (Shirky, 2011). Networked protestors helped power the Arab Spring, leading to the downfall of authoritarian regimes long believed unshakable; secret cables published by Wikileaks have exposed the details of the US foreign policy decision-making to the international public and Chinese Internet users spread photo evidence to expose corrupt local officials (Kalathil, 2013: 3). Concretely, intertwined changes in politics and mass communication have considerably expanded the media’s role in diplomacy (Gilboa, 2000). Brown and Studemeister (2001) define traditional diplomacy as the art of advancing national interests by the practice of persuasion, and go on to explain that the context of persuasion has been expanded nowadays to include anyone connected to the information’s technology.



Diplomacy is now increasingly conducted in public and thousands of embassies are indeed attempting to engage with multiple publics through social media: “Both transparency and volatility have come to define the practice of contemporary diplomacy and international relations” (Kalathil, 2013: 4). Faris (2013: 36) argues that diplomats are using social media to communicate with one another, and this “has created a new channel of state-to-state communication that promises to alter interstate relations in ways we have only begun to imagine”. Particularly, Hayden (2013: 17) argues that “the social consequences of new media technologies have indeed resulted in tectonic shifts in the attention, tools and tactics of US diplomacy”. Yet, just recently have researchers begun to ask whether social media can transform the traditional practice of governmental diplomacy (Faris, 2013). The ability to spread information increases the potential for persuasion in international relations, and thus, soft power and free information can change world politics by altering how hard power and strategic information are employed (Keohane and Nye, 1998). Soft power has been defined by Nye (2011: 21) as the “ability to affect others through co-optive means of framing the agenda, persuading, and eliciting positive attraction in order to obtain preferred outcomes”. In this sense, Arquilla and Ronfeldt (2007) have argued that the information age will continue to undermine the conditions of traditional diplomacy grounded on realpolitik in favour of a noopolitik, which relies on soft power. Noopolitik, they write, “is an approach to statecraft that emphasizes the role of informational soft power in expressing ideas, values, norms and ethics through all manner of media”. Undoubtedly, Twitter is one of the channels often utilised in the governments’ public diplomacy strategies whereby soft power is publicly applied. Governments often engage in direct communication with foreign peoples via Twitter with the aim of affecting their thinking, and, ultimately, that of their governments. For instance, hundreds of thousands of Arabs routinely get on Twitter in spirited debate with many American embassies about the US policy initiatives around the world (Faris, 2013). Twitter is an online social networking site launched in July 2006 that enables users to send 140-character messages called “tweets”. “Tweets” are public messages that can be sent to anyone by placing the @ sign before a username or a # sign before a topic. Spectacularly, whilst just 4 countries4 possessed a Twitter account in 2007, 164 countries had presence on Twitter in 2014. Golbeck et al (2010) stated that an estimate of Twitter’s growth was calculated at 1,300% in one year. Even leaders from countries with limited press freedom, such as Iran or Cuba, also have Twitter accounts. However, and perhaps unsurprisingly, some of the most authoritarian countries in the world, such as China, North Korea or Burma (Myanmar), are still out of the Twitter map today:

4 Namely, the United States of America, Antigua and Barbuda, Trinidad and Tobago and Honduras.



Figure 6

Note: Countries coloured in blue have at least one member of Government that has an active Twitter account, either personal or institutional. Source: Own construction

Figure 7

Note: Countries coloured in blue have at least one member of Government that has an active Twitter account, either personal or institutional. Source: Own construction

Countries with at least one leader on Twitter in 2007

Countries with at least one leader on Twitter in 2014



Although 86% of the countries in the world have already adopted Twitter, the media’s expanding role in diplomacy has received little attention in the relevant disciplines of communication, political science and international relations (Gilboa, 2000: 276). Taking into account the specific example of American politics, 387 members of Congress were using Twitter in 2011 (TweetCongress.com, 2011): “As the use of social media becomes ubiquitous, measures of the impact of the new medium and testable theories of its importance are becoming vital to understanding this new political environment” (Gainous and Wagner, 2013: 2). Particularly, despite the obvious significance of social media’s role in politics, research on the relationship between Twitter and governments is still underdeveloped: the novelty of social media may have contributed to this fact. Also, the highly complex interdisciplinary nature of research on media and politics has inhibited progress in the field. In this sense, my study is designed to make a modest contribution toward the understanding of what political and socio-demographic factors influence governments’ adoption of Twitter. This is an empirical question, which has not been fully tackled by other studies, and that requires further, in-depth research.

RESEARCH DESIGN The unit of analysis in this study is coded in a country-month format and the period of observation ranges from January 2006 to December 2012. The starting year is 2006 because Twitter was launched in July 2006 and the ending year is 2012 due to the unavailability of data on independent variables for the 2013-2014 period. Whilst no country of the world had an active Twitter account in 2006, a sharp increase occurred from 2008 to 2010: if around 10% of the countries were on Twitter in 2008, that figure soared up to just below 60% in 2010, and reached around 80% in 2012.



Figure 8: Proportion of countries that have presence on Twitter over the observation period.

Source: Own construction

The data analysed in this study consist of 6,128 observations. The main source of information utilised in order to estimate when a country decided to activate a Twitter account has been the “Twiplomacy” dataset (Burson-Marsteller, 2014). The study comprises the total number of Twitter accounts related to a specific country; however, this paper solely focuses on the first account created by a government, indifferently to whom it belongs. This is due to the fact that I will analyse the factors that trigger when and why a country decides to open a Twitter account. Hence, this study’s main concern is related to time, and it does not distinguish between accounts that pertain to different governmental positions. My primary interest is whether democratic countries are more likely to activate Twitter accounts. In order to assess this, I created a multivariate model that includes several political and socio-demographic control variables. I use binary logistic regression to estimate the effects of each of the predictors on the probability of Twitter adoption. To assess the substantive and not just statistical relevance of my findings, I have also calculated the effects of the main independent variables on the dependent variable. The effects were calculated by changing a given independent variable from its minimum to its maximum value while holding the other independent variables at their mean. Lastly, in technical terms, my study requires an analysis of time-series-cross-section data (BTSCS).



Beck et al (1998) identified that binary time-series-cross-section data are discrete-time duration data and time dependence can be modelled in a logistic regression by including a flexible function of time since the last event as a covariate. This function was created using the BTSCS function in the DAMisc package in R.

OPERATIONALIZATION OF VARIABLES The dependent variable In order to create the dependent variable, my main source of information about world leaders on Twitter has been the “Twiplomacy” dataset (Burson-Marsteller, 2014). The dataset provides numerous details about the number and kind of Twitter accounts associated to different world leaders; yet, I solely focused on when a world government activated its first Twitter account. Thus, my dependent variable, the probability of Twitter adoption, takes the value of 0 for all the months starting in January 2006 until the month when a given country adopted Twitter, which takes the value of 1. Observations are dropped afterwards. For instance, the United States activated its first Twitter account in March 2007, thus, from January 2006 to February 2007, the dependent variable is coded as 0 and takes the value of 1 in March 2007, and the observations for the United States after March 2007 are dropped. The independent variable The main independent variable is regime type. This variable, called “Democracy Score”, was coded from the Polity IV dataset, which possesses an annual, cross-sectional, time-series and polity-case format and shows democratic and autocratic “patterns of authority” in all independent countries with total population greater than 500,000. These patterns of authority are captured on a 21-point scale ranging from -10 (hereditary monarchy) to +10 (consolidated democracy). The Polity IV Project also explains that the Polity scores can be converted into a three regime categories: “autocracies” (-10 to +6), “anocracies” (-5 to +5) and “democracies” (+6 to +10) (Polity IV, 2014). My primary hypothesis is that all other things being equal, democracies are more likely to have presence on Twitter due to the role that communication plays in democratic states. First, Dahl (1971:1) argues that “a key characteristic of a democracy is the continued responsiveness of the government to the preferences of its citizens”.



The reason that democracies are more likely to adopt Twitter may be that one expects democratic governments to do all that is possible under the circumstances to please the citizenry, and the citizens that have an online presence surely seek to engage with the representatives they have chosen (Przweworski et al, 1999). Second, and related to this, there must be a popular involvement in the deliberative system in order to have a true democracy, and social media fosters public engagement in the decision-making process (Allison, 2002). Although democracy remains an “essentially contested concept” (Gallie, 1956), its main constitutive pillar includes citizen involvement in political decisions; thus, democracy is embodied in communicative practices (Kedzie and Aragon, 2002). That is why press freedom is one of the most important features of democratic governance (Nisbet and Stoycheff, 2011). Also, social media creates a different democratic system that moves beyond the traditional hierarchical structure of aggregative democracy towards a more deliberative way of understanding the democratic decision-making procedure (Wagner and Gainous, 2009). As a matter of fact, scholars usually rely on the concept of the public sphere, proposed by Habermas (1989), when trying to analyse the democratizing potential of social media, since this new channel allows for the reciprocity necessary in democratic politics (Papacharissi, 2010). These communicative practices become particularly important during election time in democratic regimes. In general, one would expect electoral goals to motivate Twitter adoption because elections raise leaders’ desires to communicate with their constituencies and Twitter is a means to do so (Lassen and Brown, 2010). This comes from the fact that leaders in democracies are extremely sensitive to the role of the media during campaign coordination. Political elites often use social media in order not only to raise money, but also to engage with the public sphere in ways not possible with broadcast technologies (Howard, 2005). Dobek-Ostrowska and Garlickli (2013) cite the work of Johnson (2011) who highlights that online social media is an integral part of the political campaigns in the twenty-first century and explains that online communication brings citizens new ways of participation in democratic politics. In fact, Twitter became broadly adopted during the 2008 election cycle in the United States (Senak, 2010). And a survey of the Congressional Management Foundation reported that half of the Members of Congress in the US believe that Twitter is very important for understanding constituent views and opinions (Williams and Gulati, 2010).



Lastly, there is a growing usage of new technologies in democracies in order for politicians to communicate more directly with the public. This is due to the fact that social media dis-intermediate the flow of political information to the public. Politicians strategically manipulate public goods in order to secure political survival (Bruno de Mesquita et al, 2005). In this respect, the expansion and intensification of social media use for political gain is increasingly relevant (Gainous and Wagner, 2013: 2). From the point of view of the democratic leaders, social media in general, and Twitter in particular, allow them to address citizens directly and this increases their relative power (Steele and Stein, 2002). In fact, Johnson (2004) highlights how new Members of Congress adopt social media in order to secure their new acquired political positions. There are several advantages they can derive from creating more direct links with potential voters through social media: politicians cannot only communicate directly to voters who have chosen to follow them, but “they are also able to determine constituents’ concerns more directly, without these being co-opted by the agendas of media outlets campaigning in favour of their own preferred outcomes” (Bruns and Wilson, 2010). In this respect, Gainous and Wagner (2013) also argue that information communication systems are vital to the democratic process, and social networks allow political actors to shape the political content they deliver without the dictations of the traditional media. In this respect, social media offers politicians a unique opportunity to control their message, maximizing their influence on public opinion. Control variables Various political, economic and socio-demographic indicators have been included in order to adequately account for the relationship between democracy and the likelihood of opening a Twitter account by a government. Press freedom is coded from the press freedom index that Reporters Without Borders publishes every year and measures the level of freedom of information in 180 countries for the year 2014. Scores range from 0 to 100, with 0 being the best possible score and 100, the worst. Duffy (2014) explains how the media systems of countries that impose widespread censorship are usually set up by government licensing under laws that tightly restrict information. Consequently, one would expect that an increase in the score of press freedom would translate in a reduced probability of Twitter adoption by a given government. I have also controlled for the economic development of a country. The variable “GDP per capita” has been taken from the World Bank Indicators and shows the GDP per capita of every country in 2013, measured in current US$. Poushter (2015) argues that richer countries in terms of gross domestic product per capita are much more likely to have high rates of Internet access.



Also, Allison (2002) claims that technological advancement is commonly associated with economic growth. As a result, it is expected that in countries with high GDP per capita, world leaders are more likely to activate Twitter accounts. Furthermore, Bughin et al (2014) explain that digital literacy barriers are often associated to under-resourced education systems. In this sense, even a basic awareness of the Internet can be an issue in poor countries. In order to control for this, the variables literacy rates and expenditure on education have been included. The former is coded from the adult literacy dataset provided by the UNESCO Institute for Statistics. It accounts for the percentage of the population aged 15 and older who can, with understanding, both read and write a short simple statement on their everyday life, in 2012. Also provided by the UNESCO Institute for Statistics, the expenditure on education variable measures the public expenditure on education as percentage of a country’s GDP, in 2012. Both predictors are expected to have a positive relationship with the response variable, since higher levels of education make people more comfortable with and skilled in the use of technology (Williams and Gulati, 2010). On the other hand, Mergel (2013) contends that there are three primary reasons as to why governments decide to have a social media’s presence. First, the overwhelming reason to participate in social media spaces is having a representation of the agency on all potential interaction channels. Twitter, with 500 million users in 2012, has convinced media directors that they need to adapt to the communication practices of the society they live in. Second, government departments with presence in social media have also asserted the need to interactively engage with the public they represent. Third, not only are they interested in becoming involved with the citizens, but also in listening to them. In order to control for this phenomenon, I included the variable Internet Users, which is coded by the World Bank. It measures the number of Internet users in a country, per 100 people, in 2013, and is expected to have a positive relationship with the probability of a government adopting a Twitter account. Lastly, ordinary logit analysis, which is the statistical method employed in this paper, fails to allow for temporal dependence in the likelihood of event occurrence; yet, it is unlikely that units are statistically unrelated over time when analysing time-series-cross-section data (Beck et al, 1998: 1260). Temporal dependence implies that the units of observation are correlated over time, that the value of variable x for unit i at time t is largely dependent on the time t-1,…, T. In fact, this problem becomes even more acute when the dependent variable is binary (Buhaug, 2005: 97).



That is to say, the probability of adopting Twitter for a country at any given time is likely to be affected, for instance, by simultaneous occurrences of Twitter adoption in neighbouring countries. In order to control for this phenomenon, Beck et al (1998) include dummy variables that count the number of time periods since either the start of the sample period of the previous “failure” (in this case, the activation of a Twitter account). Carter and Signorino (2010) agree with Beck et al (1998) that scholars should “take time seriously”, however, they argue that “the question becomes one of how to allow for temporal dependence in binary data without being too restrictive concerning the form of dependence” (Carter and Signorino, 2010: 274). As a result, they propose to include t, t2, t3 in the regression in order to avoid problems such as quasi-complete separation that the inclusion of time dummies may trigger (Carter and Signorino, 2010). This paper will not include time dummies or the cubic polynomial approximation suggested by Carter and Signorino. On the one hand, time dummies is sometimes the most flexible approach but also very inefficient to deal with time dependency issues when the dataset is not big enough (Carter and Signorino, 2010). On the other hand, it is generally well known that the t, t2, t3 variables are highly correlated. For large datasets this is almost never a problem (Carter and Signorino, 2010: 283), yet my dataset is not large enough. As an alternative, and using the BTSCS function in the DAMisc package in R, I created a time variable that identifies the number of observed periods since the last event. After estimating two different models, one with the logarithm of time and one with the square root of it, the former one provided the best AIC. Thus, the variable time [log(time +1], has been created and ranges from 0 to 4.43, since the original time (before logged) measures the number of months and it ranges from 0 to 83. Figure 9 provides more details about the operationalization of the variables above explained.



Figure 9: Summary Statistics. Source: Own construction

Variable

N

Mean

SD

Min

Max

Description

Twitter Adoption

6,128

1.40

0.1

0

1

Dummy variable that is coded as 0 when a given government does not have Twitter and the month when its Twitter account is activated is coded as 1

Democracy Score

6,218

3.30

5.98

-10

+10

Polity IV data, ranging from -10 (hereditary monarchy) to +10 (consolidated democracy)

Internet Users

6,128

30.31

24.11

0.9

90

Internet users, per 100 people in 2013

Press Freedom

6,128

34.63

13.62

9.63

84.83

Country’s press freedom status, ranging from 0 to 100, in 2014

GDP per capita

6,128

6,722

10,872

226

93,352

GDP per capita (current US$) in 2013

Literacy Rates

6,128

77.66

20.25

25.30

99.8

Adult literacy rates, both sexes (% ages 15 and older), in 2012

Expenditure on Education

6,128

4.46

1.97

1.2

13

Total public expenditure on education (%GDP) in 2012

Time

6,128

3.16

0.93

0

4.43

Log(time+1) ranges from 0 to 4.43



RESULTS BIVARIATE ANALYSIS I report briefly a bivariate analysis before moving on to the multivariate results in order to shed light on to the relationships between the different variables exposed above and the likelihood of Twitter adoption. As expected, the likelihood of Twitter adoption is significantly and positively related to economic development, so we would expect richer countries to adopt Twitter at higher rates. Similarly, literacy rates also exhibits a positive and significant relationship with the probability of activating a Twitter account; thus, the higher the literacy rates in a country, the higher the probability of adopting Twitter. Also, as one would expect, the higher the number of Internet Users in a country, the higher the probability a country has of activating a Twitter account in a given month. Regime type and time also have a significant and positive correlation with the likelihood of Twitter adoption. In other words, democracies are more likely to activate Twitter accounts, and the likelihood of Twitter adoption grows larger over time. Lastly, there is no significant relationship between expenditure on education and the likelihood of Twitter adoption, and neither between “press freedom” and the probability of activating a Twitter account. Table 1. Probability of Twitter adoption: bivariate correlations



MULTIVARIATE ANALYSIS Because many of the control variables can be shown to correlate significantly with the likelihood of Twitter adoption, one should expect that the introduction of controls would alter the picture. I use binary logistic regression to estimate the effects of each of the predictors on the probability of Twitter adoption. Logistic regression is appropriate because the response variable, Twitter adoption, is binary. Warner (2013: 932) argues that binary logistic regression does not have assumptions as restrictive as linear regression. First, it does not require normally distributed scores on the Y outcome variable; second, a linear relation between scores on Y and scores on quantitative X predictor variables and homogeneous variance of Y across levels of X are not required. Because it demands less restrictive assumptions, Warner (2013) concludes that binary logistic regression is more appropriate where the dependent variable is dichotomous. In this sense, logistic regression is multiple regression but with an outcome variable that is a categorical variable and predictor variables that are continuous or categorical (Field et al, 2012: 313). Maximum likelihood techniques are used to maximize the value of the log-likelihood function, which indicates how likely it is to obtain the observed values of Y, given the values of the independent variables and parameters α, β1,…, βk (Menard, 2002).

In order to interpret the results from a logit model meaningfully, the model itself must first fit the data; that is, the explanatory variables included in the model must be able to explain the response variable significantly better than the model with the intercept only. The likelihood ratio statistic, which approximately follows the chi-squared distribution, is the most widely used test in order to see whether the logit model fits the data (Liao, 1994). Table 2 shows the results after using the lrtest function in the lmtest package in R. The results compare the full model (with all the predictors included) with the likelihood of the null model (which contains only the intercept). Because p-value<0.05, one can reject the null hypothesis and confirm that adding eight parameters more to the full model has statistically improved the model. In conclusion, the likelihood ratio statistic indicates that the model fits the data significantly better than the model with the intercept only, then we can move on to interpret parameter estimates (Liao, 1994). Table 2. Results from the likelihood ratio statistic Df LogLik Df Chisq Pr(>Chisq) 1 1 -452.29 2 8 -373.61 7 157.38 <2.2e-16***



Table 3 gives thus the results of the multivariate logistic analysis for the adoption of Twitter for a given country in a given month. The coefficient estimates of a logistic regression indicate the effects of a variable on the odds of an event. A positive coefficient estimate indicates that the odds of an event increase with the corresponding independent variable, and a negative coefficient estimate shows that the odds of an event decrease with the corresponding predictor variable. That is to say, the coefficient estimates are expressed in terms of the increase or decrease in the probability of Twitter adoption by a change in the factor of interest. Table 3. Probability of Twitter adoption: multivariate logistic regression



After introducing the control variables, there is still a positive and significant relationship between regime type and the likelihood of Twitter adoption. As it has been hypothesized, democracies are more likely to activate Twitter accounts than other type of regimes. This is due to the fact that communication is one of the main pillars of a democratic regime, indispensable both in the deliberation process and in the responsiveness associated with this type of regime. Also, communication is a coordination good usually exploited by democratic politicians during election time in order to ensure political survival. Contrarily to the bivariate correlations above presented, various control variables stop being significant in the multivariate analysis. Particularly, literacy rates and GDP per capita no longer show a significant relationship with the likelihood of Twitter adoption. However, the control variable, Internet users, is still positively and significantly associated with the likelihood of Twitter adoption: the higher the number of Internet Users in a given country, the higher the probability of activation of a Twitter account for a given country in a given month. This may also be related to regime type, since, in general, fully democratic regimes impose far fewer restrictions on Internet freedom, while politically closed regimes control the Internet system in a pervasive and systematic way (Yangyue, 2014: 10). Lastly, time is still positively and significantly related to the adoption of Twitter: the likelihood of Twitter adoption for a given country grows larger over time. We can also examine how an independent variable increase or decrease the odds of an event by the concept of odds ratios. One can find the effect of each independent variable on the odds of the event by taking the natural anti-log or exponent of the coefficient. If the odds ratio is greater than 1, it indicates that the odds of the outcome occurring increase as the predictor increases. Conversely, a value less than 1 indicates that as the predictor increases, the odds of the outcome occurring decrease. Taking into account the independent variables that showed a significant relationship with the response variable, table 4 gives the odds ratios associated with them: Table 4. Odds ratio for the significant independent variables VARIABLE ODDS RATIO Regime type 1.11 Internet Users 1.04 Time 23.52



The odds ratio for the variable regime type is greater than 1, which means that the odds of activating a Twitter account are 1.11 times higher the more democratic a country is. Similarly, a value of 1.04 for the variable Internet Users translates into the fact that countries that possess a higher number of Internet Users are 1.04 times more likely to adopt Twitter. Lastly, the odds of activating a Twitter account are 23.52 times higher as time goes by. On the other hand, it is worth noting that in ordinary regression, one assumes that the outcome had a linear relationship with the predictors. However, in logistic regression the outcome is categorical and so this assumption is violated (Berry, 1993). That is why the logit of the dependent variable is utilised instead, in order to express a non-linear relationship in a linear way (Field et al, 2012: 315). In this respect, we assume that the logistic transformation on our binary dependent variable produces a linear relationship between independent variables and the logit of the dependent variable (Osborne, 2015). However, it may be the case that the relationship between regime type and the likelihood of Twitter adoption is non-linear. Therefore, a square term can be added into the equation in order to capture a curvilinear relationship between the main independent variable and the response variable. When the estimated coefficient for the independent variable is statistically significant, it means that the relationship between X and Y is indeed curvilinear. If the estimated coefficient is positive, then the relationship between the dependent and the independent variable is U-shaped, whereas if its negative, the relationship is inverse-U shaped. Table 5 shows the model presented above with a square term added into the equation (“Democracy Score Squared”), simultaneously along with the original untransformed variable (“Democracy Score”). The estimated coefficient is negative and significant, which means that the relationship between regime type and the likelihood of Twitter adoption is inverse-U shaped:



Table 5. Probability of Twitter adoption: multivariate logistic regression

To assess the substantive and not just statistical relevance of my findings, I have also calculated the effects of the independent variable and the significant control variables on the dependent variable. Firstly, the predicted probability of adopting Twitter for a given country that has the minimum number of Internet Users in 2013 is 0%, compared to a 4% when the number of Internet Users is set at its maximum, and everything else is held constant. Regarding time, we expect a stronger relationship with the probability of Twitter adoption over time (Gainous and Wagner, 2013). Thus, the probability of Twitter adoption when the variable time is set at its minimum is 0%, and it goes up to 14% when time is set at its maximum.



I am particularly interested in the substantive effect of the variable regime type. Figure 10 shows the substantive effect of the variable regime type on the likelihood of Twitter adoption, when holding all the other variables at their mean.

Figure 10. Probability of Twitter adoption for different levels of democracy. Source: Own construction As one can easily see, the effect of regime type on the likelihood of Twitter adoption is not particularly robust. The predicted probability of Twitter adoption for an autocracy (-10) is virtually the same compared to a democracy (+10). The effect of democracy is thus very small when holding everything else constant. However, the effect of the democracy score and the predicted probability of Twitter adoption become substantially robust when holding constant the time variable and the Internet Users variable at their maximum and all the other independent variables at their mean. This follows Carter and Signorino’s (2010: 272) recommendation on treating time as something that is substantively interesting. In this scenario, the predicted probability of Twitter adoption for a given country increases from 16% for an autocracy (-10) to 70% for a democracy (+10), as shown by figure 11.



Figure 11. Probability of Twitter adoption for different levels of democracy when setting the time variable and the Internet Users variable at their maximum. Source: Own construction

CONCLUSION

The analysis presented in this study provides evidence that democratic regimes are more likely to activate Twitter accounts. Since there are no previous studies on this specific topic, this paper argues that the fact that democracies exhibit a higher likelihood of Twitter adoption may be due to the central role that communication plays in democratic regimes. Governments may decide to activate a Twitter account so as to engage with the online citizenry, in order to be responsive to their needs (Przweworski et al, 1999). Similarly, the fact that Twitter offers the possibility of reciprocal engagement may trigger governments to utilise it in order to achieve a true process of deliberative democracy, whereby political decisions are made after going through a rational dialogue among all the participants (Papacharissi, 2010). Unsurprisingly, communication becomes increasingly important during election time in democratic regimes. Thus, it might be the case that democracies decide to use Twitter as a means for fundraising and persuade the potential voters. More generally, communication constitutes a coordination good that can be easily exploited by political elites in order to seek survival (Bruno de Mesquita et al, 2005). As a result, democratic leaders may adopt Twitter in order to benefit from dispersing their political messages directly without the intermediation of traditional media: they are able to determine the constituents’ concerns more directly without being co-opted by the agendas of media outlets (Bruns and Wilson, 2010).



I have also found that the relationship between regime type and Twitter adoption is curvilinear and inverse-U shaped. Further research should explore when the probability of Twitter adoption change suddenly at what points of the democracy score. The results reported here also show a positive and significant relationship between the number of Internet users in a given country and the likelihood of adopting Twitter. Perhaps unsurprisingly, the higher the number of Internet users in a country, the higher the probability of activating a Twitter account. Likewise, the likelihood of Twitter adoption grows larger over time. Also importantly, the substantive effect of the regime type variable on the likelihood of Twitter adoption is not particularly robust when holding everything else constant. However, it becomes stronger when holding the time variable and the Internet Users variable constant at its maximum, and everything else is held at their mean. The findings presented here also suggest multiple directions for future research. Political scientists need to acknowledge the role of social media and its impact on politics. Although the importance of social media in the present environment can be difficult to measure due to the rapid rates of adoption, there are numerous ways in which social media can influence the political system, and those, are still largely unclear (Gainous and Wagner, 2013). The analysis offered in this paper is an onset analysis: it explores the empirical factors as to why governments decide to activate a Twitter account. However, there is a growing phenomenon by which a government activates a Twitter account that becomes inactive after a given period of time. Further work should carry out a termination analysis, and look into the empirical factors of Twitter deactivation by world governments. More generally, there are also various lines of research that may be analysed by further research. For instance, is Twitter a new means to develop a governments’ public diplomacy strategy? Coined by Gullion in 1965, public diplomacy focuses on the influence of public attitudes on the formation of foreign policies. It emphasizes the cultivation of foreign public opinion by governments through processes of communication. A content analysis of the diverse tweets generated by various governments may shed light on whether Twitter is a central piece to the transnational flow of information and ideas. Also generally, can international public support via social media influence conflict dynamics? Widely used in the Gaza Conflict in 2002 and in the on-going Syrian Civil War, Twitter may alter governments’ strategic actions during a crisis.



Further work should also look into whether Twitter is a new means to conduct diplomatic relations. What are the differences between traditional diplomacy and diplomacy practiced on Twitter? Is Twitter a game changer, or just another communications medium? Both quantitative and qualitative studies that focus on governments’ “follows” and “followers”, “retweets”, “mentions” and “interactions” would add crucial insights into the new concept of “twiplomacy” that has been already coined without in-depth theoretical and empirical analysis on whether Twitter may indeed be a new forum for the development of diplomatic relations. A third line of research should focus on how social media is affecting the political discourse in general, and more specifically, on the role of Twitter in electoral campaigning. Political actors have begun to understand how to maximize the social media potential for communication functions. Generally, studies should analyse whether Twitter is an effective tool for political communication, yet “the pace of academic research has not been able to keep up” (Gainous and Wagner, 2013: 9). Although pundits and politicians alike have suggested that Twitter will play a major role in electoral campaigns (Lassen and Brown, 2010), whether Twitter becomes a dominant factor in influencing an electorate remains to be seen (Senak, 2010). Singularly, qualitative studies about how politicians are using diverse strategies (fundraising, supporting-seeking or information-dissemination) in order to design their electoral campaigns are missing. And there is even a greater lack of quantitative analyses that empirically measure the influence of social media, in general, and Twitter, in particular, as a means of organization and communication in democratic elections. On the other hand, future work is needed to explore whether Twitter is an adequate means to bring about processes of deliberative democracy. This model of democracy entails a reasoned, reciprocal, inclusive, egalitarian and power-free argumentation over disputed issues in order to achieve rational consensus (Habermas, 1989; Benhabib, 1996; Cohen, 1996; Rawls, 1997). Twitter is a means of communication that can instantly connect everyone, and also permits reciprocal interaction where everyone has the possibility to be heard. Can, thus, Twitter be a means to practically implement the famous Habermasian public sphere? Furthermore, the direction of the causal arrow established in this paper goes from democracy to Twitter. Yet, it has been extensively documented the role of social media during the Arab Spring. In this sense, may the causal arrow go from Twitter to democracy? Further research should establish whether Twitter may constitute a gain of social capital, being perhaps able to open closed regimes.



A final line of research should look into how international organizations are using Twitter to promote their activities and engage with their followers. Not only have governments decided to adopt Twitter, but Burson-Marsteller (2015) show how all leading international organisations have set up at least one institutional Twitter account over the past eight years. The authors of the study explain how the European Organization for Nuclear Research (CERN) announced via Twitter the discovery of the “God particle”. Also, the World Health Organization usually uses Twitter alerts to send important messages of epidemic outbreaks. This means that Twitter has become an indispensable communication tool, not only for governments, but also for international organizations. The present paper has made a modest contribution towards the understanding of the empirical factors of Twitter adoption by governments, but further research is needed in order to dig deeper into those factors and broaden the study in order to include international organizations.

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