Make Tweets Great Again: Who Are Opinion Leaders,and What Did They Tweet About Donald Trump?

Opinion Leaders: Two-Step Flow of Information

A seminal study of the 1940 U.S. presidential election revealed that most people are influenced not by mass media, but by opinion leaders: “ideas often flow from radio and print to the opinion leaders and from them to the less active sections of the population” (Lazarsfeld et al., 1969, p. 151). This concept, known as the “two-step flow” of communications, has had a profound influence in marketing, political science, and diffusion of innovations (Rogers, 2003; Shah & Scheufele, 2006; Van den Bulte & Joshi, 2007). According to the two-step flow of communication hypothesis, there are opinion leaders throughout every class and occupation. Through word of mouth, they have more influence on friends, coworkers, and relatives in decision-making processes and behavior than the mass media (Katz, 1957). Katz and Lazarsfeld (1955) suggested four major traits of opinion leaders: having a large following, being considered as an expert, being knowledgeable, and holding a central position within their networks to influence social pressure and support.

In the two-step flow of information, influential members of the public (known as opinion leaders) have played a prominent role in transferring information from news media to the public. The two-step flow of information emphasizes the role of interpersonal communication in overcoming some limitations of mass media’s direct reach toward audiences. Erbing et al. (1980) suggested that interpersonal communication was necessary to help audiences understand news media issues, arguing that opinion leaders could reinforce or reduce the effects of media messages on personal agendas.

Opinion Leaders on the Internet and Social Media

The emergence of the Internet and digital media technology has raised questions about the continued utility of the two-step flow of information hypothesis. The Internet provides an unlimited amount of information at a low cost, and users can personalize their news feeds. The Internet is also perceived by many as an egalitarian forum that can facilitate many-to-many (multiplex) interactions and reduce inequality by increasing opportunities for individual participation in online discussions. Others have not only argued that online opinion leaders exist but also that the two-step model remains important conceptually (Wu et al., 2011). While traditional opinion leaders had greater access to information than their followers, digital media changed the dynamics of information flow. Specifically, online opinion leaders can now produce information and transfer it to mass audiences. Also, online opinion leaders can be identified by the quality of arguments they make (Lu et al., 2013) and the degree of activity they exhibit in online discussions (Kwak et al., 2010).

Social media redefined the traditional concept of opinion leaders and gave birth to a new category: social media influencers. McCorquodale (2020) defined an influencer as “an individual who has built a digital audience through sharing editorialized content about their life” (p. 11). McCorquodale suggested the term influencer can be interchangeable with a content creator, blogger, Instagrammer, and YouTuber; such figures are known as independent, industrious, and famous in digital environments. McCorquodale (2020) suggested that influencers do share information from traditional media, but their information-sharing activities reflect their own views and perspectives. Influencers are seen as having more direct engagement with their audiences than traditional opinion leaders. Furthermore, influencers’ social media activities can also lead their audiences to gravitate away from traditional media toward social platforms.

Twitter Opinion Leaders: Unique Characteristics and Their Association With Polarization

Twitter influencers have unique characteristics. McCorquodale (2020) suggested that Twitter users who have significant followings are motivated to propel their ideas into mainstream media. Twitter influencers have been known to have controversial and extreme voices that tend to encourage other users to react to them. Moreover, the “value” of opinion leaders’ tweets has been evaluated based on their reach and diffusion (Bruns, 2012).

Journalism is also affected by these tweets. For example, by tweeting controversial posts, opinion leaders on Twitter can get an editor’s attention and coverage, for even a retweet can expose opinion leaders to mainstream media audiences (McCorquodale, 2020). In a process originally known as the reversed two-step flow (Brosius & Weimann, 1996), the public’s interests can be transferred to the mainstream media through opinion leaders who first spotlight issues. Also, some journalists are encouraged to be commentators on Twitter by discovering stories not covered in mainstream media, commenting on existing stories, or eliciting additional reports from their audiences (McCorquodale, 2020). Lastly, the boundary between news and comments has been blurred on Twitter to generate further audience growth and engagement. Thus, Twitter has the ability to redefine news agendas and issues by adding multiple personal opinions through retweets.

On Twitter, opinion leaders can be celebrities, political actors, journalists, and other influencers. For example, the Twitter activities of celebrities can and do receive attention from news media and the public (Lang, 2017). Tufekci (2013) argued that celebrities, as spokespeople or supporters of a social movement, could perform as event headliners, and their activities could be highlighted in traditional and social media. Walter and Brüggemann (2020) argued that political actors could spark Twitter discussions and initiate interactions by posting a high volume of tweets and/or by being mentioned by other Twitter users. Walter and Brüggemann found that political actors could impact Twitter debates about climate change, as their tweets were retweeted by activists, scientists, and journalists. Journalists can utilize Twitter to brand themselves and their organizations (Molyneux et al., 2018). Journalists have also been able to build more personal relationships with their audiences by retweeting humorous tweets, for example (Molyneux, 2015). Such Twitter usage could deviate from traditional journalistic practices that require neutrality. Some news organizations like Reuters set strict social media guidelines controlling Twitter usage by journalists to safeguard objectivity and impartiality (Jukes, 2019). However, scholars identified many journalists who can and do use Twitter liberally and have successfully built audiences on the platform through methods of self-promotion (Lasorsa et al., 2012; Molyneux, 2015).

Habel (2012) defined the opinion sections of The New York Times and The Wall Street Journal as spaces for opinion leadership, as these sections served as a venue for media elites to persuade others. Similarly, the Twitter accounts of news organizations can be considered as individual entities, engaging in direct communication with their audiences and, ultimately, fostering opinion leadership. Fourth, van Haperen et al. (2018) examined the #not1more campaign (a Twitter social movement against immigrant deportations) and found that this movement was supported by activists who actively tweeted with other users to organize online protests.

Empirical research on social networks sheds new light on the two-step communication model and provides evidence of opinion leaders’ importance, particularly on Twitter (Cha et al., 2010; Park & Kaye, 2017; Wu et al., 2011). There has been a diversity of measures to identify the influence of a user on Twitter: followers, retweets, mentions, favorites/likes, Page Rank, and Klout, for example. Moreover, rankings of influencers are highly variable, as there are many differences among the measures used (Cha et al., 2010; Riquelme & González-Cantergiani, 2016; Winter et al., 2020).

Twitter opinion leaders can also facilitate the creation of echo chambers, a situation where information is shared to reinforce a group’s preexisting beliefs (Sunstein, 2009). Echo chambers can be shaped both intentionally and unintentionally. The former is particularly seen in cases where bots are involved. At a theoretical level, the concept of reinforcement is useful and overlaps with the definitions of opinion leadership and selective exposure. Lazarsfeld et al. (1969) argued that opinion leaders were self-assured enough to assert why they were right, and they were reminded that other people agreed with them. Echo chambers were found on Twitter during the impeachment process of ex-Brazilian president, Dilma Rousseff. Specifically, opinion leaders with higher followings facilitated echo chambers by producing a high volume of retweets (Soares et al., 2018). Examples such as these highlight how Twitter networks can reinforce partisan political perspectives through active information sharing within clusters of networks. In some cases such as this, like-minded followers produce and consume media within a partisan bubble. However, in many other cases, filter bubbles were not found to exist on Twitter (Eady et al., 2019).

Scholars have examined Twitter networks and studied various communication patterns, including polarization (Bastos et al., 2018; Pew Research Center, 2014). “Polarized crowds” represent two big and dense groups in a network that were rarely connected. In the U.S. context, polarized crowds often refer to liberal and conservative groups with limited interconnections. Within each densely connected polarized crowd, Himelboim et al. (2013) found evidence of “hub” users who occupied the center of each cluster and acted as a sort of clearinghouse of information.

In this study, we sought to identify who opinion leaders were in the Twitter mention network of former President Trump. In order to understand which topics were most prominent during Trump’s first 30 days in office, we used the computational method of topic modeling. Lastly, we evaluated whether echo chambers emerged in the Twitter network we studied, and we explored whether topics were discussed differently between the two dominant U.S. political parties—Republicans and Democrats. Thus, we considered the following research questions in this study:

Research Question 1: Who were the opinion leaders tweeting about Donald Trump during his first 30 days in office?

Data, Opinion Leaders, and SNA

We examined Twitter data to identify the opinion leaders and the topics related to Trump during his 1st month in office. We collected 2,862,987 unique tweets from January 20 to February 18, 2017, that contained the word “trump.” These tweets were posted by 1,260,752 unique users from all over the world and include non-English-language tweets. Within these data, we further identified a subset of tweets associated with user accounts with 10,000 or more followers and studied the user profiles most mentioned by these accounts. The 10,000-follower cutoff was used as it would not be possible to manually study a data set with over a million unique users. Of course, opinion leadership of a user is not necessarily linked to their number of followers, and accounts with few followers may be opinion leaders. However, for a profile to be influential, it is essential that it be mentioned by users with many followers (through retweets, replies, etc.; Park & Kaye, 2017; Shao et al., 2018).

In our sample, 133,640 tweets (around 5% of the initial sample of 2,862,987 tweets) were from users with 10,000 or more followers. These tweets were posted by 36,540 unique users. We used SNA to systematically identify connections among actors and investigate evidence of polarized clusters in our sample. SNA can be used to study individual nodes (actors including persons or organizations), ties (an edge or connection among nodes), and subnetworks (parts of a larger network; Ward et al., 2011; Wasserman & Faust, 1994). In this study, the unit of analysis is an individual Twitter account, which we treat as a single node/actor in the entire network.

Measurements of centrality evaluate how highly concentrated links are around a small number of central nodes. In SNA, various types of centrality are used to quantitatively measure the role and relative importance of individual nodes within a network (Hanneman & Riddle, 2005). Valente (2010) found that node centrality can be used successfully to identify opinion leaders in social networks. There are multiple ways of calculating centrality scores, such as betweenness, degree (in and out), closeness, and eigenvector centralities (Everett & Borgatti, 2005; Freeman, 1978). Eigenvector centrality—a measure used to describe the relative importance of nodes—can be used to identify the most central actors who have the smallest degree of farness from others in the overall structure of the network. This measure assigns higher weights to links that connect a node to other central nodes.

We used Gephi version 0.9.2, an open-source SNA software package, to measure the eigenvector centrality of the nodes in the network. We created a mention network to observe how specific users were mentioned by extracting targets (nodes being mentioned) and sources (nodes who mentioned other accounts). A total of 49,182 Twitter accounts were found in the mention network, based on 133,640 tweets posted by 36,540 unique users. We then enumerated a list of eigenvector centrality scores in descending order (from highest to lowest) to rank order opinion leaders in the mention network.

Among the 49,182 unique users, 38% (N = 18,862) of the users mentioned had zero eigencentrality. From the 30,320 users that scored above zero, the top 1% (N = 303) tweeted 5,856 times from January 20, 2017, to February 18, 2017. The subsample of 303 users was manually coded to identify types of opinion leaders based on each user’s Twitter profile and their tweet content. We classified opinion leaders into 12 categories: news media, nonmedia organization, journalist, political analyst, Republican politician, Democrat politician, right-wing activist, left-wing activist, celebrity, a politician from another country, bot, and suspended user. The categories are grounded in data and were created after analyzing user accounts. We opted for a broad and mutually exclusive categorization to facilitate the analysis.

News media (newspaper, news agency, etc.), nonmedia organizations (foundation, nonprofit organization, etc.), journalists (reporter, producer, etc.), political analysts (contributor, columnist, etc.), celebrities (musician, actor, etc.), Republican politicians, Democrat politicians, and politicians from other countries generally identify themselves as such in their Twitter bio and often have verified accounts. To classify activists, we studied the posts of each user and found they were generally monothematic and explicit in terms of their beliefs and views.

In order to identify bots in our data set, we queried Botometer (https://botometer.osome.iu.edu/) for all user accounts. We labeled as bots the users that scored above 4.8 out of 5 since only 9% of users with this score are human. Botometer is a tool that uses a machine-learning model trained to classify an account as a bot or human; it has been successful in detecting social bots (Davis et al., 2016; Ferrara et al., 2016). We defined “suspended” users as those with accounts deleted or suspended by Twitter (e.g., for violating the terms of service). Although many additional accounts have been suspended since we began our research, we kept these accounts in the initial categories since they were active at the time of our data collection. In the “Results” and “Discussion” sections, we mention accounts that were suspended after we collected the data.

Bessi and Ferrara (2016) found that during the 2016 presidential election campaign, bots were responsible for one fifth of political discussions on Twitter. Among tweets posted by bots, 30% of Twitter accounts were suspended for violating the terms of service. Bessi and Ferrara (2016) argued that such a prominent number of tweets posted by bots could influence political discussions and public opinion. Thus, we added bots and suspended users to the categorization of opinion leaders.

Topic Modeling

We used the software package MALLET version 2.0.8 (McCallum, 2002) to conduct topic modeling. This system uses probabilistic algorithms to computationally identify topics from large text corpora. Topic models have been successfully applied to multiple text mining tasks to discover groups of related words in collections of tweets (Murthy, 2015). These baskets of words are called “topics.” Latent Dirichlet Allocation (LDA; Blei et al., 2003) is one of the most popular topic models and has been successfully applied to study a diverse range of topical domains on Twitter (Weng et al., 2010; Zhao et al., 2011).

We chose LDA topic modeling because it is one of the few methods available for deciphering topics from large tweet corpora and because it has been used to successfully study tweets within the context of political communication (Karami et al., 2018; Sharma et al., 2017; Zheng & Shahin, 2018). Our topic modeling analyses were iterated. Specifically, we iteratively extracted sets of 50, 25, 15, 10, and five topics, with and without any optimization, from the 5,856 tweets from the top 1% of opinion leaders. We did this until recognizable clusters of topics emerged. We also used MALLET with the same settings with the entire sample. Although tweets were collected in several languages, we found that the main topics in these were either proper nouns or words in English.

Lastly, we measured the degree of polarization of tweets mentioning Donald Trump. We calculated modularity scores in the network and modularity classes for each user through Gephi. Modularity is a measure to determine the extent to which calculated clusters are bounded (Himelboim et al., 2013) and can be used to identify communities or clusters in networks. We used a computational algorithm created by Blondel et al. (2008) to calculate modularity scores and enumerate clusters produced by similarities of members in each cluster. Modularity scores range from −1 to 1 (Li & Schuurmans, 2011). Scores closer to 1 have dense connections between nodes within clusters but sparse connections between nodes in different clusters (Li & Schuurmans, 2011). Most modularity scores range from 0.3 to 0.7 (Newman & Girvan, 2004). In the context of polarization, low modularity scores ranging from −1 to 0.3 have been found to imply that no polarization exists in the network, while high modularity scores ranging from 0.3 to <1 have been found to indicate the existence of polarization (Garcia et al., 2015). Himelboim et al. (2013) indicate 0.6 as a threshold for detecting high divisions and 0.4 as a sufficient threshold for a medium level of divisions among clusters. After calculating modularity scores, we interpreted opinion leaders’ tweets in each group and evaluated whether detected clusters were polarized or not.

Who Are the Opinion Leaders Tweeting About Trump?

About a quarter of opinion leaders are media outlets or self-described as news sites. It should be noted that some of these accounts (e.g., @BreitbartNews and @zerohedge) have been flagged as consistently spreading misinformation or fabricated news by fact-checking organizations. The next less frequent category are journalists at 18% of our sample. Next were right-wing activists and Botometer-flagged accounts (both made up 13% of our sample). Table 1 provides a breakdown of all opinion leader categories by frequency and percentage. Figure 1 illustrates the mention network between these accounts.

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Table 1.

Types of Opinion Leaders Tweeting About Trump During His 1st Month in Office (by Frequency and Percentage).

Type of Opinion Leader	Users (N = 303)
News media	69 (23%)
Journalists	55 (18%)
Right-wing activists	38 (13%)
Bots	38 (13%)
Celebrities	28 (9%)
Democrat politicians	15 (5%)
Left-wing activists	14 (5%)
Political analysts	13 (4%)
Nonmedia organizations	11 (4%)
Suspended accounts	10 (3%)
Republican politicians	8 (3%)
Politicians from other countries	4 (1%)

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Figure 1.

Influencers’ mention networks. Note. Accounts with highest eigencentrality are rank ordered.

News Media

News outlets were distributed in the following categories: extremely biased left-wing, left, left-center, right, right-center, extremely biased right-wing. Following Bovet and Makse (2019), we used the websites www.allsides.com and www.mediabiasfactcheck.com to perform our classification. We also used the latter to measure the factual reporting of the news organizations: from very high to very low.

Around 90% of media users are “verified” accounts, which means they are accounts verified by Twitter as being in the public interest or considered to belong without doubt to verified humans (Ferrara, 2017). As Table 2 indicates, most media outlets are left-wing (e.g., @CNN, @RawStory) and left-center (e.g., @nytimes, @washingtonpost). The organizations classified as center (e.g., @AP, @TheEconomist) were the best performers in following traditional, fact-based journalism in that 92% of them scored high/very high for factual reporting. The right-wing media (e.g., @FoxNews, @DailyCaller) and the right-center (e.g., @FoxBusiness, @FortuneMagazine) scored 6% and 4%, respectively, and none of these media scored high/very high for factual reporting.

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Table 2.

Media Outlets Tweeting About Trump During His First Month in Office (by Frequency and Percentage).

Media Bias	News Media	Factual Reporting (Very High or High)
Left	20 (29%)	5 (25%)
Left-center	20 (29%)	18 (90%)
Center	13 (19%)	12 (92%)
Extreme bias (right)	5 (7%)	None
Right	4 (6%)	None
Right-center	3 (4%)	None
Undefined	4 (6%)	None

Among the most influential media users’ accounts, there are none from the extreme left-wing, but there are five extreme right-wing accounts that also score low/very low for factual reporting: @Breaking911, @BreitbartNews, @gatewaypudint, @infowars, and @zerohedge. All these websites have been flagged as consistently spreading misinformation or fabricated news. @infowars and @gatewaypudint were permanently suspended from Twitter for violating the platform’s rules (Conger & Nicas, 2018; Dellinger, 2021).

Journalists and Political Analysts

With 55 accounts, journalists represent a little less than a fifth of the opinion leaders during our study period. About 96% have an account verified by Twitter and work or collaborate with news organizations. About 70% are linked to left-wing or left-center media outlets, while 14% are associated with media classified as center and 11% with right-wing or right-center publications. Finally, 5% work for extreme right-wing websites, which promote conspiracies and fake news (e.g., @BreitbartNews, @gatewaypudint). Journalists are associated with 28 media outlets, but 62% are associated with CNN, and The New York Times. All the right-wing opinion leaders are associated with Fox News.

Political analysts have different backgrounds, but they can be grouped into two categories: those linked to media outlets and those that are independent. Around 70% fit the first case. Six opinion leaders are linked to left-wing or left-center news organizations (e.g., @arimelber), and three are linked to right-wing or right-center media (e.g., @jasoninthehouse). Among the opinion leaders who do not have a clear association with any media outlet are @tribelaw and @igorvolsky.

Right- and Left-Wing Activists

Together, the activists represent 17% of opinion leaders, with 52 users. Those labeled as activists users were users who tweeted about politics, generally in a partisan matter. Among the right-wing activists, only one user (@ScottPresler) has an account verified by Twitter. Of the 38 right-activist opinion leaders, 18 had their accounts suspended for violating Twitter’s rules, and another eight deleted all their tweets and changed their profile picture. In other words, 68% of the right-wing activists who tweeted in favor of Trump during our study period no longer exist. Some of these accounts, such as @SandraTXAS, @ChristiChat, @asamjulian, @nia4_trump, @JrcheneyJohn, @MissLizzyNJ, @ThePatriot143, and @jko417, also played a prominent role on Twitter during and after the 2016 presidential election, often spreading disinformation and fake news (Al-Rawi et al., 2019; Bovet & Makse, 2019; Cornfield, 2017; Zheng & Shahin, 2018). We also found that our sample of leaders included the suspended account @TEN_GOP, an account affiliated with the Russian IRA, which engaged in information operations campaigns on Twitter and other social media platforms.

The left-wing activists consist of 14 users (5% of opinion leaders), with one verified by Twitter: @LibyaLiberty. Since 2017, one account has been suspended for violating Twitter’s Terms of Service, and another three deleted all their tweets and changed their profile pictures; these three represent 28% of the left-wing opinion leaders in our study. As with the now-suspended right-wing opinion leaders’ user accounts, left-wing suspended/deleted accounts played a prominent role on Twitter during and after the 2016 U.S. election. Users such as @ezlusztig and @IMPL0RABLE were among the few that appeared among the influencers on Twitter in 2016, which was generally dominated by right-wing activists (Jaech et al., 2018; Nguyen et al., 2017).

Bots and Suspended User Accounts

We labeled as bots the accounts that scored above 4.8 out of 5 as scored by Botometer, given that only 9% of users with this score are human. These 38 users were responsible for 11% of the total tweets during the analyzed period. Twenty-nine of the 38 bots had common attributes: They were created between February and September 2016, they always tweeted from TweetDeck, and they retweeted the remaining 28 accounts several times. By publishing in a coordinated way, this group managed to influence the top-performing topics during the analyzed period with tweets such as “Nude Photos of Melania Trump Published in the New York Post https://t.co/9rhmogSzHl and “J.K. Rowling Burns Donald Trump in 3 Magical Tweets https://t.co/WR0HRwYDxY.” All accounts are now suspended by Twitter, except: @just_lovforever, @RitikaSarma1, @govindkumarbab1, and @mahimarani21. The last tweet from these four accounts was posted on January 25, 2018.

There is not much to comment on regarding the 10 accounts that were suspended right after our data collection since we did not inspect these users. That being said, 3% of opinion leaders in our sample were suspended by Twitter for violation of their terms of service in 2017. At the time of writing in March 2021, that number reaches almost 30%. Together, bots and users that are suspended (again, as of the time of writing) accounted for a quarter of all tweets posted during our study, which corroborates levels of bots and suspended accounts found by others (e.g., Bessi & Ferrara, 2016).

Democrat and Republican Politicians

Among the opinion leaders we identified on Twitter, 15 users are linked to the Democrats and eight to the Republicans. Most opinion leaders among Democrats are senators—a third of the total (e.g., @SenSanders and @SenSchumer). But there are also congressional representatives, institutional accounts, and a former president. Among Republicans, the majority of opinion leaders during our study period were White House employees (e.g., @KellyannePolls and @DanScavino), followed by senators, congressional representatives, and institutional accounts. All these accounts are verified by Twitter.

Other Opinion Leaders

Four non-U.S. politicians are among the opinion leaders we studied: the Prime Minister of Israel, Benjamin Netanyahu (@netanyahu); Nigel Farage (@Nigel_Farage), the then Member of the European Parliament; Theresa May (@theresa_may), the then Prime Minister of the United Kingdom; and Vicente Fox Quesada (@VicenteFoxQue), the then President of Mexico. The first two are well-known Trump allies. Trump had meetings with all but Fox Quesada (who had mocked the border wall) during our data collection period. All tweeted about Trump in English.

Among the 11 nonmedia organizations (i.e., organizations other than news media), there are accounts considered neutral (e.g., @YouTube, @Nordstrom), and there are institutional accounts (e.g., @WhiteHouse, @SecretService). Finally, among celebrities’ accounts, there are television shows that regularly parodied Donald Trump, such as Saturday Night Live (@nbcsnl) and The Daily Show (@TheDailyShow), as well as celebrities tweeting for and against Trump (e.g., actor @RealJamesWoods and the writer @StephenKing).

Main Topics Addressed in Tweets by Opinion Leaders

Two groups were created to conduct LDA analyses: one with 2,862,987 tweets that contained the word “trump” and the other with 5,856 tweets from our subsample of influencer tweets. Table 3 indicates that the President’s executive order on immigration at the end of January (Stack, 2017) was the main subject discussed in our study period. Figure 2 provides a timeline that illustrates the frequency of tweets among the influencers that contain the word “trump” per day from January 20 through February 18, 2017.

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Table 3.

General Summary of the Top-Performing Topics.

Opinion leaders’ tweets	trump; president; realdonaldtrump; donald; melania; published; photos; executive; order; travel; ban; obama; white; house; russian; flynn; wall.
All tweets collected	trump; president; donald; trump; potus; ban; order; white; house; america; inauguration; court; travel; people; cnn; obama; news; media; twitter;

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Figure 2.

Timeline of Tweets from influencers by frequency, from January 20 to February 18, 2017.

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Figure 3.

Twitter network mentioning “Trump” during the first 30 days of former President Trump’s presidency. Note. For visualization purposes, the nodes of networks are Twitter accounts with eigenvector centrality scores of 50 or more. The size of the nodes is equal to their eigenvector centrality.

To get a more accurate analysis of the main topics of the first 30 days of Trump as U.S. President, we repeated the LDA analyses on the two groups in five different periods: January 20–22, January 23–29, January 30 to February 5, February 6–12, and February 13–18. Table 4 illustrates the terms/“topics” discerned by LDA as most relevant (typos and hyperlinks, such as “https://t.co,” “amp,” and “https” have been removed).

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Table 4.

Summary of the Top-Performing Terms/Topics in Five Different Moments in 2017.

Date	Opinion Leaders’ Tweets	All Tweets Collected
January 20–22	realdonaldtrump; trump; president; donald; trump’s; inauguration; photos; showed; york; times; j.k; rowling; womensmarch; magical; lady; super; melania; published; nude; hot	Trump’s; trump; president; donald; inauguration; inaugural; day; people; womensmarch; march; women; today; protest; obama; crowd; made; america; divide; white; big
January 23–29	Trump’s; trump; president; executive; ban; published; literally; rowling; burns; magical; tiffany; melania; photos; nude; inappropriate; york; mexican; order; realdonaldtrump; grab	Trump’s; trump; president; donald; executive; ban; order; wall; news; immigration; muslim; people; america; white; house; obama; day; inauguration; media; muslimban
January 30 to February 05	Trump’s; realdonaldtrump; immigration; president; travel; order; ban; people; obama; america; trump; supreme; judge; senate; executive; muslimban; court; country; russian; realmuckmaker	Trump’s; ban; order; immigration; people; president; trump; donald; house; muslim; travel; judge; obama; america; white; executive; court; administration; news; realdonaldtrump
February 06–12	Trump’s; immigration; travel; ban; realdonaldtrump; president; ivanka; trump; donald; putin; media; white; house; order; court; russia; moscow; news; gop; snl	Trump’s; travel; ban; court; news; realdonaldtrump; president; trump; donald; obama; people; america; white; house; ivanka; nordstrom; melania; immigration; anti-trump; time
February 13–18	Trump’s; president; russia; russian; donald; trump; gop; white; press; media; obama; flynn; flynn’s; resignation; national; adviser; fake; news; immigration; mar-a-lago	Trump’s; flynn; media; trump; donald; president; russia; news; press; campaign; obama; white; house; people; security; russian; cnn; putin; administration; gop

How Are Twitter Networks Related to Former President Trump Divided?

To evaluate how Twitter communities talking about former President Trump are divided, we calculated modularity scores using Gephi. We also interpreted tweets posted by Republican and Democratic influencers to investigate how tweets could contribute to the polarization of Twitter communities. Based on a total of 49,182 users in the mention network, we observed a total of 4,926 communities. The modularity score was .684. Based on Garcia et al.’s (2015) criterion (modularity scores range from 0.3 to <1: the existence of polarization), and Himelboim et al.’s (2013) criterion (0.6 as a threshold for detecting high divisions among communities), communities in the mention network are highly divided.

Next, we compared both Republican and Democrat opinion leaders’ tweets by week to compare how opinion leaders in both communities tweeted about Trump differently. Our observations identified that though Twitter opinion leaders discussed some of the same topics, they did so very differently based on their political orientation.

From January 20–22, the main topics discussed on Twitter were “inauguration” and “women’s march.” Some Trump supporters posted tweets celebrating Trump’s presidential inauguration, while others who opposed Trump’s election posted about their participation in the “women’s march” in Washington, DC. For example, @SandraTXAS tweeted on January 19, 2017, “THIS is my president! Donald Trump lays wreath at Arlington Cemetery #Inauguration #MAGA#TrumpInauguration,” and @funder tweeted on January 21, 2017, “SOME may not be his-but the burden of proof is on #Trump, not me #trumpleaks #theresistance #womensmarch #inauguration #thankso.”

Twitter opinion leaders on the left retweeted posts by bots, including satirical posts by J.K. Rowling, a British author, who had a feud with Donald Trump on Twitter. One tweet, “J.K. Rowling Burns Donald Trump in 3 Magical Tweets,” was posted by bots on January 21, 2017 and was then retweeted extensively. During the week of January 23–29, the main topics discussed on Twitter were related to Trump’s travel ban on people from a list of predominantly Muslim countries. In terms of tweets mentioning this executive order, we found evidence of polarized discussions based on this issue within tweets mentioning former President Trump. For example, @SpeakerRyan tweeted on January 23, 2017, “My statement applauding President Trump’s first executive actions,” and @KamalaHarris tweeted on January 25, 2017, “Trump’s executive orders on immigration today will tear apart families at the direct expense of public safety and national security.”

These two tweets provide an example of polarized opinions about the executive order posted by two prominent political figures, Paul Ryan and Kamala Harris. Other tweets posted during this week included topics such as “photos of Melania Trump” and “grab” that reflected attacks on Trump’s family and criticisms of former president Trump’s audio-recorded remarks about sexually assaulting women. These tweets were generally posted by bots and tended to focus on more sensational issues.

During the week of January 30 to February 5, Trump’s 2nd week in office, the travel ban was the dominant topic among both sets of general users. Opinion leaders posted tweets on this issue that were indicative of their political orientation and again reflected polarized positions. For example, @SenWarren tweeted on February 1, 2017, “Muslim countries PERMANENT on ban Israeli Jews. Yet they say we are intolerant?? #ImmigrationOrder #maga #Trump,” and @SandraTXAS tweeted on February 5, 2017, “No, @realDonaldTrump, your illegal, unconstitutional, immoral Muslim ban is not about keeping us safe at all.”

During the week of February 6–12, along with the legal fight over Trump’s travel ban, the main topic was “Russia,” referring to the investigation of potential political connections between Donald Trump and Russian leader Vladimir Putin. Media organizations and workers did present contrasting opinions about this issue. The topic of potential Russian interference in the 2016 U.S. presidential election is reflected in the following tweets. For example, @thehill tweeted on February 10, 2017, “Trump says he doesn’t know about reports Flynn talked about sanctions with Russia,” and @DavidCornDC, an MSNBC analyst and the author of Russian Roulette, tweeted on February 9, 2017, “I think it can now be said: Trump and Flynn have sold out US democracy to Putin and Russia.” Among the influencers, “SNL” relates to Saturday Night Live, a television comedy show that regularly parodied Trump and his administration. For example, @businessinsider tweeted on February 10, 2017: “Alec Baldwin: Why it’s ‘not a lot of fun’ to play Trump on ‘SNL.’” This tweet mentions a skit on the SNL television show where Trump, parodied by the American actor Alec Baldwin, expresses that playing Trump is not actually a “fun” endeavor, despite SNL being a very popular comedy show.

During the week of February 13–18, the Affordable Care Act (also known as “Obamacare”) and former president Obama were dominant topics. As in previous weeks, evidence of polarized discussions shaped by political opinion leaders was found. For example, @steph93065 tweeted on February 18, 2017: “For Obamas entire term the media lied to us about O’Care, the economy and Iran deal; they are now lying to us about Trump.” In addition, @SenWarren tweeted on February 15, 2017: “@realDonaldTrump & the @GOP should stop these reckless actions to hurt the ACA before they hurt even more people who need health coverage.”

During this week, Russia also remained a dominant topic. “Russia,” “putin,” “national,” “security,” “adviser,” and “flynn” were found in tweets and were related to Michael Flynn’s resignation as national security adviser because of his contacts with the Russians (Borger, 2017). For example, @BostonGlobe tweeted on February 14, 2017: “Trump was told weeks ago that Michael Flynn had not told the truth about his interactions with Russia’s ambassador.”