Political Discourse on Social Media
Trolls, Bullies, Bots, and the U.S. Presidential Election in 2016
While our previous research has examined the ways in which social media can empower historically disenfranchised groups, racial minorities, and affective networked publics (see Blevins et al., 2019), we now look at the ways in which social media conversations about race turn politically charged and, in many cases, ugly. A Pew Research Center study by Anderson and Hitlin (2016, August 15) showed that social media is an important venue for news and political information, while focusing national attention on racially involved issues.
In fact, two of the most used hashtags around social causes in Twitter history focus on race and criminal justice: #Ferguson and #BlackLivesMatter...and key news events...often serve as a catalyst for social media conversations about race. (Anderson & Hitlin, 2016)
Perhaps less understood, though, is the effective quality of this discourse, and its connection to popular politics, especially when Twitter trolls and social media mobs go on the attack.
Social media mobbing occurs when groups of people converge on Facebook, Twitter, and other social media platforms around an issue that they are angry about, or a person that offends them. The mob relentlessly trolls that person, or dominates discussion of the issue with a barrage of insults, arguments, and memes. Some of the more notable targets of social media mobbing have been black women, including Saturday Night Live comedian Leslie Jones, Olympian Gabby Douglas, and the mother of a young boy who fell into a gorilla exhibit at the Cincinnati Zoo (see Blevins, 2016, August 28).
This calls into question the quality of public discourse taking place in certain venues on social media. Since there are no universally accepted community standards online, conversations taking place via social media can occasionally escalate into a mob-like atmosphere in which the more even-tempered speakers are heckled off the platform in a rabble of highly offensive posts, including some that are explicitly racist. For instance, Leslie Jones, who used Twitter to speak out against sexism and racism, eventually deleted her account under the crushing emotional strain of the social media mob that was trolling her with crude attacks about her appearance (see Fisher & McBride, 2016, July 20).
In another case, the Cincinnati Zoo asked social media users to stop posting memes about Harambe, the silverback gorilla that was put down in order to save the life of the child who fell into the exhibit, because it was hurtful to their staff. Nevertheless, the Zoo’s call for self-restraint only ignited the mob further, which in turn overwhelmed their social media feed with even more posts and memes involving images and mentions of Harambe. As a result, the Zoo deactivated its Twitter account to escape the disquiet (see Chan, 2016, August 23).
A concern present in all of these cases is that the voices in favor of more respectful public dialogue on social media may tend to spiral into silence for fear of being mobbed. As such, social media outlets should consider more carefully how they want to define and enforce community standards for their own platform. In the Leslie Jones case, Twitter eventually banned the mob leader from using its service. But that is just one high-profile incident. More generally across social media platforms, action against mobbing occurs on a case-by-case basis. At the very least, the application of policies seems inconsistent, and it is usually up to the target of offensive posts to initiate the complaint.
In some cases an organized social justice group can help balance out the discourse, such as when @BlackLivesCincy and @theIRATE8 quickly mobilized on social media under the hashtags #SamDuBose and #JusticeForSamDuBose to challenge the framing of DuBose’s shooting as justified and were able to help balance public understanding about the meaning and implications of the slaying (see The Irate 8’s website, which includes a timeline of events: https://www.theirate8.com).
However, when there appears to be no mobilized social justice effort, the results can be far different. For instance, there was an uneven display of empathy on social media toward black and white parents of children involved in tragic incidents during family outings in 2016. A Black family was visiting the Cincinnati Zoo on May 28, 2016, when their three-year-old son climbed over a barrier and fell into a gorilla exhibit, encountering a 450-pound silverback named Harambe. Despite being dragged around the child was not seriously injured, as zoo staff shot and killed the gorilla within minutes (see McPhate, 2016, May 29).
Afterwards, the child’s mother was widely vilified in a barrage of memes and tweets tagged #JusticeForHarambe for not responsibly looking after her child (see Blevins, 2016, July 9–15). An online petition quickly collected a half-million signatures asking that the mother "be held accountable for her lack of supervision and negligence" and further requested a criminal investigation as to whether this was "reflective of the child's home situation." There was widespread outrage that a gorilla was killed due to an "idiot mom" and speculation that she was "shopping for lawyers and celebrating her good fortune.” One of the more popular memes pictured Harambe with the caption: "Why did they shoot me? I was doing a better job watching that lady's kid than she was." Rallying under the #JusticeForHarambe hashtag a stream of social media posts seemed to mock the slogans invoked in recent social justice movements after black males were slain by white police officers (e.g., #JusticeForSamDebose, #Justice4Dontre, #JusticeForTamirRice, and #JusticeForJohnCrawford, etc.).
In contrast, similar incidents involving white families did not provoke the same kind of vitriol on social media. In another instance, tagged #DisneyGatorAttack, a two-year old boy was killed by an alligator at a Disney resort on June 14 while he splashed around by himself in a shallow lagoon a short distance away from his parents (see McLaughlin et al., 2016, June 16).
There are some important distinctions (besides race) that would account for the more tempered reactions on social media in the latter case. The young child could not be saved, and the parents were suffering unimaginable anguish. Also, the Disney gator attack happened just days after the deadliest mass shooting in U.S. history at the time, which dominated the national news cycle, as well as social media activity (see Ellis et al., 2016, June 13).
Another accident happened at the Cleveland Zoo in 2015 when a White mother dropped her child while dangling him over a cheetah exhibit (see Denson, 2015, April 12). The boy suffered a broken leg and the mother received probation as a result of the incident. However, there was no widespread campaign on social media to further humiliate the mother, which is in stark contrast to what happened to the black mother at the Cincinnati Zoo.
While it may be difficult to quantify the disparity in social media reactions between these cases, it is not impossible to see the difference. Whether intentional or not, posters often seem to jump to conclusions based on minimal information contained in a meme or tweet, and may further perpetuate insidious forms of racism with hasty likes, shares, and retweets.
While trolls and mean tweets certainly add to the quantity of dialogue taking place on social media, we may at the same time question the quality of this form of public discourse. Ideally, in a society that values free expression, online networks should be a platform for opposing thoughts and viewpoints in a digital marketplace of ideas, without devolving to the communicative equivalent of throwing rocks at each other. Rather, as Johnson (2016, p. 215) has suggested, “digital communication intermediaries like Facebook or Twitter should publish community standards that commit to protecting freedom of expression on their platforms in all but a few clear exceptions,” such as threats of violence, pornography, or other criminal content. Too often, online hecklers do not face their opponents person-to-person, but manage to silence them through threats and intimidation. They can be online mobs “who petition Facebook to remove speech with which they disagree” or take to Twitter and other outlets “to intimidate others who speak up for a cause” (Johnson, 2016, p. 219).
The value of social media as a potential tool for public discourse is that individuals and groups can send their message to a large number of other individuals and groups that are both intended (and not intended) to receive the message, thus contributing to the marketplace of ideas. Addressing this point, Johnson (2016, p. 219) warns that information intermediaries have to be careful to not “allow norm policing and trolling to be amplified to such an extent that it chills potentially valuable speech.”
There is already evidence that a small amount of the content online is receiving a large amount of attention. On the internet, audience “attention is clustered around a select few content options, followed by a long tail, in which the remaining multitude of content options each attract very small audiences that in the aggregate can exceed the audience for the ‘hits’” (Napoli, 2010, p. 5). As Johnson (2016) explained, “this difference in scale is problematic for public discourse,” as the further one’s speech is down the long tail, the smaller the marketplace for one’s ideas.
Given the rise of Twitter trolls and the ability of some groups to shout down other more reasoned voices in sensitive questions about race, we might also question who is driving traditional political conversations, especially during election cycles. We examine this particular issue using Twitter activity during the U.S. presidential election cycle in 2016, which was also a high point for Twitter-trolling and cyber-bullying in the Harambe and Leslie Jones cases discussed previously.
In previous works we studied social justice networks and affective publics from the bottom up (see Blevins et al., 2019), and now, we look at “gaming” the social media networks for political ends. Our case study is the 2016 presidential election in the U.S., where we visualize the structure of Twitter networks from political hashtags across the ideological spectrum (e.g., #MAGA, #ImWithHer, #FeelTheBern, etc.). We look at network structures across the political spectrum, and examine how different political tactics lead might lead to different network structures. Moreover, we look at the extent to which trolls and bots might influence these Twitter networks, as well as the impact that individual Twitter posters had during critical moments of the campaign, especially during the presidential debates between candidates Donald Trump and Hilary Clinton. A related purpose of this set of data visualizations of Twitter activity is to measure the relative popularity of tweets from Russian-based trolls and bots compared to those made by the candidates, celebrities, and other social media influencers. For instance, a Pew Research Center study predicted that it is bots that post about two-thirds of tweeted links to popular websites, rather than humans (see Wojcik, Messing, Smith, Rainie & Hitlin, 2018, April 9).
Similar to the methodology described in Blevins et al. (2019), this set of Twitter data visualizations is derived from an automated extraction process developed in Python 2.7, which allows us to search for specified terms in the Twitter historical archive, including such things as hashtags and words in tweets. The program stores these results, sorted by day, as a flat JSON file, formatted in two ways, with one data structure for exploring tweet-retweet relationships, and another data structure for viewing basic descriptive statistics about the search terms. Tweet-retweet relationship data structure builds arrays of nodes and links based on the Twitter historical archive. Nodes consist of users in the searched data, and links are built between those users and others who retweeted them. In this process, we preserve important data, such as tweet text and time of tweet, for more detailed exploration. These nodes and links are visualized using a modified D3.js force-directed graph that is filterable by time. We built a data visualization dashboard with the crossfilter.js and dc.js libraries for filtering and visualization; the descriptive statistics visualizations allow for interactive explorations of these data. Further, we use a dynamic/interactive version of these graphics.
The data visualization of the pro-Trump tweets show what we call a “continent effect”—a massive conglomeration of activity around pro-Trump hashtags (most notably #MAGA). Looking at the time period around the three presidential debates between candidates Trump and Clinton, pro-Trump tweets tend to amass in large continents on Twitter, as compared to pro-Clinton tweets that (at most) form small islands. Interestingly, however, the pro-Clinton tweets were actually greater in number to the pro-Trump tweets, but were more widely dispersed. In some sense this is analogous to the election results, in which Clinton wins the popular vote but loses in the Electoral College based on how votes amass within individual states. While the number of pro-Trump tweets were fewer than the pro-Clinton ones, they were arguably a more impactful force, and are more noticeable in their conglomeration on Twitter. Compared to pro-Clinton tweets, pro-Trump tweets were more cohesive as they created immense nodes (which, ironically, are in contrast to Clinton’s campaign message of “Stronger Together”). In terms of Twitter activity, pro-Clinton tweets were scattered apart. Comparing these data visualizations on Twitter to the story elements of the campaigns may show us something that traditional polling failed to predict in the election outcome. At the very least, Twitter visualizations are another way to make sense of the election that the polls did not.
To understand the “knockout” experiments we conducted with this data set, and to perform your own tests with this data set, go to the resource provided here. Here you can observe the growth of several subnetworks, which appear to be right-wing influencers that are not part of the Trump campaign. In essence, the subnetworks of pro-Trump nodes have metastasized and become a highly resilient network overall. From our experiments, even if you take out the center node from the Trump campaign, the broader network is still there. Some of the most prominent of these subnetworks are nodes formed by the “bfrazer747” node, the “TeamTrump” node, and the “USA4Trump” node.
From these experiments, more subtle and insidious form of influence become visible. Beyond election messaging, the use of bots challenges the utopian view of social media and social networks, as large nodes can be artificially manufactured with deceptive actors trying to game the system.
Trolling in the 2016 Twittersphere: A Closer Look
Based on the research previously presented, we sought to explore how politically right-leaning entities appear to be more effective at using Twitter to promote their political inclinations and presidential candidate. The previous results (illustrated in Figures 1–13) might indicate that this is the result of troll activity, especially how pro-Trump Twitter accounts overwhelmed the node around Hillary Clinton (see Figure 13). However, this is still only an assumption about the power of trolls and bots in monopolizing and manipulating the Twittersphere in support of candidate Trump during the 2016 election.
Our subsequent analysis presented below is based on data visualization methods that stemmed from questions about the influence of troll-like behavior from real users who are utilizing troll tactics to support their positions (e.g., inclusion of emojis alongside texts, the insertion of random plug-ins regarding unrelated topics, and the use of extreme and aggressive language).
Definition of Terms
In this subsequent analysis we use the term “right-leaning” or “the right” to categorize Twitter handles supportive of candidate Trump, as well as handles expressing anti-Hillary Clinton, anti-Democratic Party, or anti-Obama statements. While many express pro-Trump sentiments directly by positively referencing the Twitter handle @realDonaldTrump, others express their support by lambasting Hillary Clinton while simultaneously using “left-leaning” hashtags such as #ImWithHer alongside the use of right-leaning hashtags such as #MAGA.
We use the terms “left-leaning” or “the left” to categorize handles with pro-Hillary Clinton content. More often “the left” is represented by handles strongly opposing Donald Trump.
This subsequent study includes 1 percent of Twitter data from the date ranges of September 25–27, 2016, and November 6–8, 2016, and examines the data in three ways:
- We examine the centrality of the major nodes in both degree and betweenness, as represented through numerical data and visual representations of these networks.
- We look at the co-occurrence of the major hashtags during these same two time periods, where tweets from both the left and the right are analyzed to determine the use of hashtag co-occurrence by parties supportive of either side of the debate. The top 50 hashtags for betweenness and degree are used to determine the frequency of co-occurrence. Numerical graphs as well as bar graphs are used to dictate both the amount of hashtags used by each group and the rate at which each utilizes co-occurrence within their network. Combined with the bar graphs, networks of the left and right, as well as the larger network, where the top eight hashtags are highlighted, provided visual representations of co-occurrence.
- We performed a series of “knockout experiments” in which major nodes within the larger network are removed to determine their influence on the broader network. Visuals examine the larger network within September and November and highlight the major nodes essential to the structure and stability of the network. In addition to knockouts, this section includes visuals where major nodes are highlighted to present and compare their places within the network.
When looking at the centrality of specific Twitter handles throughout September 25–27, 2016, we found that @realDonaldTrump (the official Twitter account of candidate Trump) was the highest in degree (0.0456), and @HillaryClinton (the official Twitter account of candidate Clinton) was the second highest in degree (0.0311) (see Figure 2.1):
Figure 2.1: Handles highest in degree for September 25–27, 2016
As further seen in Figure 2.1, a majority of the handles that are highest in degree are pro-right handles (e.g., realDonaldTrump, DanScavino, LindaSuhler, bfraser747, magnifier661, TeamTrump, CarmineZozzara, The_Trump_Train, and StatesPoll). These major nodes hover around the two candidates as the first debate on September 26, 2016, unfolds. These major handles attract the most Twitter volume and draw the most attention to the debates surrounding their favored candidate Donald Trump.
Donald Trump’s official Twitter account, @realDonaldTrump, also rates highest in betweenness (0.0990), while Hillary Clinton’s @HillaryClinton (0.0805) places second (see Figure 2.2).
Figure 2.2: Handles top in betweenness, September 25–27
As illustrated in Figure 2.2, the top handles in betweenness are mostly supporters of the right. Again, this visual showcases the strength of the right within the network and their role as an integral part in connecting others to the network. Some of the same handles highest in degree resurface as highest in betweenness. Again, most are those supportive of Trump and the right.
Also telling is the use of hashtags by the candidates during the September 25–27, 2016, time period: around the first presidential debate. In Figure 2.3 we can see that the #MAGA hashtag is the most dominant. However, @HillaryClinton does not use any hashtags in her post, except #LoveTrumpsHate, and only once during the days encompassing this first debate. As noted in Figure 2.4, the major green node in the center representing @HillaryClinton remains the standard node color, green, meaning this node does not contain recognizable or popular hashtags; it is surrounded by various pro-right nodes. Therefore, we might infer that the @HillaryClinton handle maintains its high ranking (second within the network) due to the many right-leaning handles who use her handle as a mechanism to attract viewers to their own views.
However, @realDonaldTrump utilizes the #MAGA hashtag in several of his posts, as well as #MakeAmericaGreatAgain and #TrumpTrain. Many of the other pro-right handles employ these same hashtags in their own tweets—and alongside several left-leaning hashtags, which will be addressed later when examining co-occurrence.
Figure 2.3: Visual of the September 25–27 network
Now let us look at the data and visualizations from November 6–8, 2016, which was the three-day period leading up to the election. Again, @realDonaldTrump rates highest in degree (0.0533), but @HillaryClinton drops down to third (0.0226). Moreover, Donald Trump, Jr. (@DonaldTrumpJr), candidate Trump’s eldest son, has moved into second place.
Figure 2.4: Handles highest in degree, November 6–8, 2016
As seen in Figure 2.4, the majority of handles that are highest in degree are pro-right handles (@realDonaldTrump, @DonaldJTrumpJr, @DanScavino, @LouDobbs, @EricTrump, @LindaSuhler, and @TomiLahren). Similar to the previous period of September 25–27, the major nodes of activity hover around the two candidates, as the major handles attract the most twitter volume and draw the most attention. The most noticeable difference in the November 6–8 time period, though, is that the second highest in degree is another Trump supporter, @DonaldJTrumpJr, who had overtaken the other candidate in the race— @HillaryClinton – within the Twittersphere. While there is also a growth of some left-leaning celebrities, such as @ladygaga and @rihanna, they are seen in the periphery, rather than at the center of the action (see Figure 2.6 for more visual details).
Additionally, two of the handles in Figure 2.4, besides @realDonaldTrump (@DanScavino and @LindaSuhler) are present among the top handles for degree in both the September and November time periods. @HillaryClinton is the only repeated left-leaning handle in both periods. This may help to explain the strength of the right and their ability to create a more inclusive and stronger network. These handles could be constituted as “loyal followers” since they play a leading role in September, while also maintaining their place within the network for the month of November. This is not the case with the left, whose supportive handles play a less consistent role within the Twitterverse. Although the support for the left within this snapshot seems to become more equitable than it was for September (meaning there are more Twitter handles who are left-leaning among this list), the lack of consistency mentioned above may account for the strength of the right’s network, despite the fact that more left-leaning hashtags are coming out to support their candidate. Perhaps they came out too late, or simply did not conform to similar themes and messages to be effective?
Figure 2.5: Handles highest in betweeness, November 6–8, 2016
As Figure 2.5 shows again, many of the handles are pro-right and vary in their hashtags (discussed later in the section on co-occurrence). Similarly, two of the pro-right handles (besides @realDonaldTrump), @DanScavino and @LindaSuhler, are present in the top handles for betweenness for the September and November periods; and again, @HillaryClinton is the only repeated left-leaning handle. Again, this is not the case with the left, whose supportive handles play a less consistent role within the Twitterverse.
Figure 2.6: Visualization of the November 6–8 network
Figure 2.7: Visualization of the November 6–8 network
Figure 2.8: Visualization of the November 6–8 network
Figures 2.7 and 2.8 show how central Trump is to the network; however, the various nodes surrounding both candidates are right-leaning handles. In comparison, many on the outskirts are the top left-leaning supporters in terms of degree. In this visual (Figure 2.8), @HillaryClinton (the green node below @realDonaldTrump) is still central to the network, but is virtually dwarfed by the density of clusters and individual nodes surrounding @realDonaldTrump. Additionally, when comparing these two major handles and their edges, it is clear that more edges stem from @realDonaldTrump compared to those stemming from @HillaryClinton. While @HillaryClinton contains several edges connected to nodes along the periphery, @realDonaldTrump is much more connected to other nodes, where their connections through distinct edges are prominent.
Co-occurrence of Hashtags
We took the top 50 handles in betweenness from the time period September 25–27, 2016, to address the co-occurrence between these hashtags. Across the top and down the left side are the nine most popular hashtags (see Figure 2.9). Each box includes the number of times the hashtag along the left side is paired with the hashtag across the top. From left to right diagonally going down are the total number of hashtags within this network (see Figure 2.9).
Figure 2.9: Co-occurrence of top hashtags in betweenness
The two most popular hashtags among the top 50 handles include #MAGA and #ImWithHer. #MAGA occurs in 26 of these top 50 handles, whereas #ImWithHer is present in just 5 handles. #NeverHillary also outnumbers #ImWithHer with 12 hashtags present among these top 50 handles as does #TrumpTrain with 7 hashtags and #TrumpPence16 with 8 hashtags. Co-occurrence among #MAGA is also much higher than #ImWithHer. Looking at the first column under #MAGA in comparison to the column for #ImWithHer (outlined in red, see Figure 2.10) it is clear that #MAGA as a hashtag co-occurs with other hashtags far more than does #ImWithHer. In fact, the only two hashtags that co-occur with #ImWithHer are both right-leaning hashtags (#MAGA and #TrumpTrain). The rest do not co-occur.
Figure 2.10: Percentage chart for co-occurrence: September 25–27, 2016
Figure 2.11: Hashtag co-occurrence chart
Figure 2.11 highlights the comparisons between co-occurrence from the left and the right. As the bar graph shows, Trump supporters, or those representing the right, are highly skilled at layering their hashtags. For instance, in Figure 2.11 it is clear that the term MAGA ranks highest in terms of co-occurance. Each color represents a hashtag paired with each one of the other hashtags. The amount of color visible among representations of each hashtag signifies the percentage of co-occurrence between these two hashtags. For instance, MAGA is sixth in comparison to other right-supporting / Trump-supporting hashtags, such as #NeverHillary and #MakeAmericaGreatAgain; the left-supporting hashtags, #ImWithHer and #NeverTrump, experience very little co-occurrence. Additionally, #NeverTrump is accompanied by only Trump-supportive hashtags #MAGA and #TrumpPence16. Most notably, #MAGA is the most popular of the hashtags and is paired with the most other hashtags, including neutral ones like #Debate2016, and has a multiplier effect by using combinations of hashtags for one tweet.
Figure 2.12: Top 50 in degree, September 25–27
Similar to Figure 2.9, Figure 2.12 identifies #MAGA as the prominent hashtag used among the top 50 handles in degree, among the 32 hashtags present in this instance. Again, the top used was #MAGA, followed by #NeverHillary (second), #TrumpTrain (third), #TrumpPence2016 (fourth), and then #MakeAmericaGreatAgain (fifth). All of these top handles are right-leaning and do a much better job of co-occurring with one another.
Figure 2.13: Percentage chart for highest in-betweenness
Figure 2.14: Highest in degree
To measure highest in degree between September 26–27, 2016, we took the top 50 Twitter handles and looked at the co-occurrence between these hashtags. In Figure 2.14, across the top and down the left side are the eight hashtags, which are color-coded and note the highest in popularity. Each box includes the number of times the hashtag along the left side is paired with the hashtag across the top. The #MAGA hashtag dominates, towering over the rest with 32 uses; it is frequently paired with #NeverHillary, and again has a multiplier effect by using combinations of hashtags for just one tweet. Additionally, the neutral hashtag #Debate2016 is paired with #MAGA five out of six times, whereas it is only paired with the pro-Hillary hashtag, #ImWithHer, one of six times. The right is much better at grouping using neutral hashtags.
Additionally, the right has more overlap in its use of hashtags in comparison to the left, as the hashtags on the network only include the top in betweenness and degree, meaning that there are fewer hashtags to compare between pro-left hashtag users. Consider Figures 2.15 and 2.16 below:
Figure 2.15: All left-leaning popular hashtags, Sept. 25-27, 2016
Figure 2.16: All right-leaning popular hashtags, Sept. 25–27, 2016
The left-leaning hashtags are represented as nodes of certain colors (colors representing hashtags) where matching colors hover around a major node. Two examples include the major yellow nodes, which consist of primarily of handles connected to these major nodes using these same major hashtags. Additionally, while there is some interconnection among the major nodes and the followers surrounding them within the left, overall they appear only sparsely connected. There are a lot of empty spaces within the network and weaker connections between each node (see Figure 2.15).
However, when looking at the right-leaning hashtags (see Figure 2.16) a much more interconnected network is visible, as each node seems deeply intertwined with the communities of the other nodes. There is barely any “empty space” between these major nodes. Additionally, while the main color of this network is pink, representing “Election Day” (this is because of co-occurrence, where the #ElectionDay hashtag has replaced the hashtag before it), the colors surrounding each of the nodes consist of multiple colors representing the other hashtags. This is very different from the left, since it is clear that the hashtags are much more diverse around the major nodes. Rather than only attracting handles or followers using the same hashtag, these major nodes on the right are able to attract those using various hashtags. This also reflects the success of co-occurrence used by top handles, since the major nodes are attracting followers that use various hashtags within their post as opposed to followers using the same hashtags.
When comparing Figures 2.15 and 2.16, it seems as if the color combinations flip. While the left is more diverse in the colors or hashtags that occupy its major nodes, the right consists of large pink nodes (representing the #ElectionDay hashtag), as many of the hashtags overlap and co-occur with other hashtags. The opposite is the case for the left, where the different hashtags/colors are seen among the major nodes. This indicates that less of the major nodes and their hashtags are co-occurring, since we don’t see a lot of the same color in a majority of this node. Additionally, the colors or hashtags surrounding these major nodes seem to differ markedly between the left and right. The left has many hashtags with similar colors surrounding the major nodes, which indicates that there is less communication between major nodes or communities where different hashtags are paired. In contrast, there is much more communication occurring between the right nodes, where multiple hashtags surround each of the major nodes. Again, this an indication that both the handles following these major nodes and the major nodes themselves are successful in pairing hashtags and therefore attracting followers using multiple hashtags, sometimes even many at the same time.
Figure 2.17: Normal network, September 25–27, 2016
Figure 2.18: Network without @realDonaldTrump, September 25–27, 2016
When looking at Figures 2.17 and 2.18, there appears to be little change when the @realDonaldTrump handle is removed from the network. Outside of the individual nodes once surrounding the @realDonaldTrump handle, many of the major connections appear to remain within the network. The edge networks also appear to remain intact, as the less significant handles and the edge networks are connected to other major nodes within the network.
Figure 2.19: Network without @HillaryClinton, September 25–27, 2016
Similar to the removal of @realDonaldTrump (Figure 2.18) the removal of @HillaryClinton (as illustrated in Figure 2.19) does little to disturb the network. While those hovering around this former node have dispersed, their connections to other major nodes have allowed these less significant nodes to maintain their space within the overall network. Furthermore, the rest of the network remains unscathed by the removal of @HillaryClinton from the network. While this may seem to suggest that the @HillaryClinton Twitter handle is unimportant within the network, @realDonaldTrump’s equally lackluster impact on the network after its removal suggests the importance of other major nodes/handles within the network and their ability to sustain support for their prospective candidates despite their candidates’ absence from the network.
Figure 2.20: Network without @realDonaldTrump and @HillaryClinton, November 6–8, 2016
Figure 2.21: Network without other major nodes, November 6–8, 2016
Even without several of the major nodes (see Figure 2.20), the right’s network remains visibly intact. While the candidates’ Twitter accounts, @realDonaldTrump and @HillaryClinton, have some influence on the network, they are not the only major nodes keeping the network together. This suggests that strong political networks are made up of several independent nodes that support their preferred candidate, but also build a fan base (or community) around themselves. These make for a stronger network, where the major node, @realDonaldTrump, is not necessarily essential for the stability of the network.
When looking at Figure 2.21, many of the major nodes on the right (@EricTrump, @TeamTrump, @LouDobbs, @LindaSuhler, @rudygiulianiGOP, @WDFx2EU8) are removed along with @realDonaldTrump. Here the overall network is visibly starting to collapse. Nonetheless, even with @realDonaldTrump gone and with many of his top followers on the right removed, the network is able to exist and still contains many Trump-supportive hashtags.
Figure 2.22: Right-leaning Twitter handles highlighted among the top 25 handles in degree for November 6–8, 2016
Figure 2.22 shows the major Twitter nodes supporting the right among the top 25 hashtags in degree. These nodes are highest in degree in the larger network, as well as in their connection to other parts of the network. Highlighted hashtags include only those supportive of the right. These nodes, or Twitter handles, include @DonaldJTrumpJr, @DanScavino, @LouDobbs, @EricTrump, @LindaSuhler, @TomiLahren, @TeamTrump, @rudygiulianiGOP, @Lrihendry, @WDFx2EU8 (which may be a handle belonging to a bot, or troll), @WeNeedTrump, @ChristiChat, @bfraser747, @mike_pence, @Stonewall_77, and @LaraLeaTrump. In comparison to the left-leaning handles, these nodes form a clear circle around the two major candidates (see Figure 2.22) and can be seen actively participating in connections not only between these major candidates and other supportive nodes, but also interacting with and connecting to one another, helping to form a circle, or rather star-like shape, encircling the candidates’ nodes (@realDonaldTrump and @HillaryClinton).
Comparing this to the major left-leaning nodes highest in degree (see Figure 2.23), the right-leaning handles within the network almost resemble a SWAT team surrounding the two major candidates. It also seems like some of the major left-leaning supporters (again, mostly celebrities) fall outside the periphery of the graph as demonstrated and discussed below:
Figure 2.23: Left-leaning Twitter handles highlighted among the top 25 handles in degree for November 6–8, 2016
In Figure 2.23 above, the following handles are highlighted: @rihanna, @ladygaga, @NormaniKordei, @ChrisEvans, @ddlovato, @JLo, and @thatbloodyMikey. It is clear that many of these nodes outside of @ladygaga (in the upper left corner) and @ddlovato (the one highlighted edge from the upper righthand corner) do not have a strong connection to the network. Many of these left handles consist of celebrities and are found along the periphery. In fact, outside of the two handles mentioned, many of these handles cannot be seen. It also seems as if they are attached more strongly and centrally to @realDonaldTrump, rather than @HillaryClinton, despite the fact that they are more supportive of candidate Hillary Clinton and use hashtags supportive of her.
In comparison to the right-leaning hashtags, these nodes are far removed from the conversation, figuratively and literally. There is not only disconnect from the main participants in the network, but there also appears to be little conversation occurring between these major nodes. This speaks to the lack of left-leaning nodes among the top 25, but also addresses the inability of these nodes to speak to the larger issues and more popular hashtags promulgated throughout the network.
From the data presented here, the right is far better at gaining attention and traction within the network as demonstrated in the visuals and numerical value attributed to the betweenness and centrality of right-leaning hashtags. Meanwhile, bots and major news outlets are not prominent within the network, as the majority of the top ranking handles (as dictated by betweenness and degree) are prominent Twitter users, such as politicians, or others with ties to the presidential race, including celebrities (especially during the November 6–8, 2016 range).
The right does a much better job of pairing hashtags alongside one another within their tweets. This is a product of the right using more hashtags, and also because the prominent hashtags in the network consist predominantly of right-leaning hashtags. Additionally, the right in several cases links left-leaning hashtags, such as #ImWithHer alongside #MAGA, in ways that promote their candidate of choice, while deriding his opponent.
While the handle @realDonaldTrump is a major figure within the network, the figures presented here, as well as the knockout experiments, show the strength of the right within the network even when their protagonist, Trump, is completely absent from the network. This is indicative of the strength of the right’s Twitter network. In fact, there was no major node that, when removed, was able to dismantle the network alone. It would take the removal of several of these major nodes for any distinguishable changes to occur.
That said, given the prominence of fake news that employed bots during the election campaign cycle of 2016, we might question what impact they had. While the right demonstrates a strong Twitter network in and of itself, we now consider the more difficult-to-measure impact of fake news, bots, and doublespeak on the Twitterverse throughout 2016, and lessons social justice movements might take from the right in their use of social networking applications.
Fake News, Bots, and Doublespeak
The internet, mobile telecommunications, and social media platforms have become integral not only to our social lives but also to how we get news and information. A Pew Research study (2016) last year showed that 67 percent of Americans get news from social media, which is up 62 percent from the year before. This presents not only a challenge for the institution of journalism but also a crisis in how our society discerns information and truth in public discourse. Knowing the difference between real and fake news has become increasingly problematic with the proliferation of tweets, memes and so-called alternative facts. With open platforms on social media, anyone can share practically anything with everyone without any fact-checking filters, making it difficult to separate credible sources of information from specious ones.
Moreover, “bots” allow users to automate hundreds of posts from a single social media account within a day, and spread false information from fake news sites at levels that can make it appear legitimate. “Bots” is short for “robots”; these are automated software programs that operate on social media platforms. They perform specific tasks, like making posts, giving the appearance of representing a real person interacting and engaging on social media. For instance, two of the most popular conservative Twitter pundits during the 2016 presidential election campaign, “Jenna Abrams” and “Pamela Moore,” were eventually found out to be Russian trolls, manufactured by the now infamous Internet Research Agency. The “Jenna Abrams” account had over 70,000 followers, and combined with others from the same farm had immeasurable reach, if not effect, as bots can give the illusion of viral popularity for particular political perspectives and candidates.
Although bot-generated messages don’t exercise mind control over social media users, when cranked out by the thousands upon thousands, they do allow certain ideological frames about timely political issues and candidates to gain saliency over others (e.g., “crooked Hillary,” “lock her up,” “build the wall,” “liberal media,” “Pizzagate,” etc.). Bot-generated tweets and ads posted on Facebook or Instagram from a broad array of accounts—“Jenna Abrams,” “Pamela Moore,” “Army of Jesus,” “Heart of Texas,” and many others—provide their own digital echo chamber and manufacture the prevalence of a certain kind of thinking, including thinking about issues related to elections as well as social justice.
The exploitation of fake news on social media became a point of controversy in the 2016 U.S. presidential election cycle after reports about Russians micro-targeting key voting districts in Ohio, as well as in Wisconsin, Michigan, and Pennsylvania, with fake news stories on Facebook. Whether or not there was political intent to sway voters’ opinions, there was nonetheless a commercial incentive to produce stories with salacious headlines that would cater to individual biases, and more importantly, draw “clicks.”
Bloomberg News reported that an engineering manager at Twitter discovered a hoard of Russian and Ukrainian spam accounts in 2015, but the company did not delete them because doing so could be interpreted as a decline in popularity of the social media network. In Congressional testimony, the estimated number of fake accounts from Russia was reported to be over 36,000. For its part, Facebook estimates that at least 146 million of its users were exposed to advertisements purchased as part of a Russian campaign. Again, it is difficult to directly quantify the impact of those ads, but it does point to a more significant problem presented by a culture that is losing its ability to recognize and appreciate epistemic rigor.
Epistemology refers to an understanding of how we know things. For instance, science, and the scientific method, is one of the most regimented ways of knowing, as it produces empirical knowledge that is based upon direct and measured observation of specified phenomena. University professors and investigative journalists can be recognized as members of epistemic communities, as they employ empiricism in the discovery of knowledge and truth. The problem presented by fake news is that it presents a sophistic kind of epistemology in which knowledge is based on what one wants to believe and is merely rooted in clever tweets and memes. As described by Plato, sophists used crafty rhetoric to make logically flawed arguments persuasive. Today we see sophistry as a form of post-truth discourse, where biases shape all news and information, and thus one source is no more or less valid than any other. No matter what the actual truth may be, there are “alternative facts” that offer another explanation consistent with one’s own worldview or self-interest.
George Orwell (1949) is credited for presenting the concept of “doublespeak” in his novel 1984, about a dystopian society in which “Big Brother,” the leader of the ruling party of a totalitarian state, uses what is termed “doublethink” to present patently hypocritical ideas as normal: “war is peace,” “freedom is slavery,” and “ignorance is strength.” Orwell also coined the term “newspeak” as an ideological form of language that ultimately leads to unclear reasoning. Although Orwell never used the term “doublespeak,” it has become fashionable to merge his concepts of “doublethink” and “newspeak” into a single term—“doublespeak”—referring to a form of rhetorical deception.
President Donald Trump regularly engaged in his own brand of doublespeak when describing any reporting about his Administration that happens to be unfavorable as “fake news,” in addition to his denigrating attacks on journalists and journalism. From the campaign trail to the Oval Office, Trump has stoked hatred and distrust of legacy news media by frequently describing the press and reporters as “dishonest,” “scum,” “horrible people,” “sleaze,” and “the enemy of the people.” These slurs are particularly troubling as they put actual fake news on an equal platform with journalistic institutions. In Trumpian doublespeak the real news is fake.
During Congressional hearings about fake news on social media, some started looking to the law to address the problem. However, the First Amendment provides a broad right of free expression, except in very rare circumstances such as blackmail, fraud, incitement, and child pornography. Moreover, most fake news content is likely to be regarded as “political speech,” which is at the heart of what the First Amendment is supposed to protect. Rather than trying to regulate false speech, it has been fashionable in U.S. jurisprudence to apply the “marketplace of ideas” metaphor, and trust in the self-righting process. Supreme Court Justice Louis Brandeis said that the way to counter falsehood, fallacies, and lies is with more speech—speech that is true.
However, fake news and Trumpian doublespeak may have exposed a fatal flaw in the marketplace-of-ideas metaphor, as the truth doesn’t necessarily emerge in a bot-generated barrage of sophistic tweets, posts, and memes. Moreover, Brandeis asserted that the news media is key to fostering an educated and well-informed public to make the marketplace of ideas work. In the post-truth world, though, the institution of journalism is the central target of doublespeak and its status is diminished.
Perhaps a public that values accurate and credible information is needed before journalism can perform its epistemic function. Only educated news audiences can critically distinguish sources of information, and take responsibility for what they communicate to others, whether it’s a share, a like, or a retweet. Only then will journalists be able to reclaim their status as an epistemic community, allowing audiences to consider the meaning of what might otherwise seem to be isolated bits of information. Before we can have an institution that successfully supports the exploration of truth, we need an audience with the critical literacy to appreciate that something can be true based on evidence, even if it is counter to our political worldviews. Otherwise, we will be left to swim through a digital marketplace of commercial and political appeals that will manufacture versions of reality in their own interest.
As the subsequent political economic analysis to be presented as a part of this project will show, the growth and distribution of fake news via bots on social media during the 2016 U.S. presidential cycle, along with doublespeak about what is considered “fake news,” has had a detrimental impact on the institutional effectiveness of journalism, and has exposed an epistemic flaw in the oft-cited “marketplace of ideas” metaphor used in First Amendment jurisprudence. We will consider the impact on political discourse and social justice efforts.
Social media, social problems, and social justice
Given the political economic limitations of the digital marketplace of ideas described in this chapter, particularly in relation to national politics, questions must be raised about how social media is prone to manipulation around social problems and social justice efforts. Just as fake news played a critical role in the 2016 presidential election, similar concerns are present in the realm of social justice, as the largest Black Lives Matter page on Facebook was found out to be a fake one (see O’Sullivan, 2018, April 9).
In January 2019 a fake account on Twitter flamed controversy around a Covington, Kentucky Catholic high school, with a viral video showing students wearing “Make America Great Again” hats while confronting a Native American man in Washington, D.C. on the National Mall. A caption with the post claimed that the students were yelling “build the wall.” It turned out that the account that initially posted the video was deactivated it because it checked several boxes for being a fake account. Ostensibly, the account belonged to a California teacher, but Twitter determined that it actually originated from Brazil and had an inordinate number of followers (approximately 40,000) for a non-celebrity, similar to the fake “Jenna Abrams” account described earlier.
Later, another (and much longer) video emerged from a group of Black Hebrew Israelites, who were present during the incident on the National Mall in Washington, and provides more details, context, and nuance than the original video that went viral. Furthermore, the longer video that emerged after national outrage focused on the Covington Catholic High School students contradicted claims made in the original viral post, as the Black Hebrew Israelites were taunting Native Americans and the Covington students before the Native American man (later identified as Nathan Phillips) is clearly seen marching into the crowd of Covington students.
Another way to look at the incident that took place on January 18, 2019, is that there were three different groups of people (high school students from a conservative Catholic high school in Kentucky, Native Americans, and Black Hebrew Israelites) from widely different cultures, with contrasting world views, exercising their First Amendment rights on the National Mall in ways that (in at least in two cases) were patently offensive. Absent social media and the original viral video, this would not be a news story.
However, with social media, a single fake Twitter account was able to ignite national outrage over the incident by presenting it within a narrow frame of cultural politics. When the later video emerged, it showed that the students were not the only aggressors in the incident; this did little, however, to quash widespread anger at the students. What seemed to bother social justice advocates was the disparity in how the White youths targeted by trolling after the initial video were treated with broader empathy after the longer video emerged—something that would not seem possible for Blacks in somewhat similar situations, such as the Harambe incident in Cincinnati, which occurred a few years earlier.
Social media is the digital frontline of confrontations, where groups of people immediately stake out entrenched positions in the immediacy of a moment—and then vigorously defend that position, rather than engaging in discourse and moving to potentially changing positions based on new information, context, and nuance. Social media tends to present an epistemology of memes, mean tweets, trolling, and bullying, while legacy news media struggles to catch up and provide the context and nuance with more information to better understand events taking place. Rapid-fire exchanges on Twitter and Facebook spread like wildfire.
In the Covington Catholic incident, it appeared that an entity (outside of the three that were actually involved) manufactured a version of reality and stoked emotions in such a way that no one wanted to concede any ground in their understanding of the event. Even in the aftermath of the confrontation, spectators on social media seemed closed off to perspectives and facts that contradicted their position about the event. Those who were originally critical of the Covington Catholic students claimed that legacy reporting of further details that emerged was an attempt to whitewash the incident and excuse otherwise boorish behavior by the students. Indirectly, it also presented a challenge to journalistic norms, which are to cover all sides of a story and provide as much information as possible. This further eroded trust in news media. In process, the artificial nature of the viral tweet in conjunction with the refutation of additional information about the event in D.C. gives some a means to dismiss legitimate claims of social injustice. Accordingly, the next chapter examines the manipulation of social justice activities on social media.
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The Irate 8: https://www.theirate8.com.
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Tornoe, Rob. “Two popular conservative Twitter personalities were just outed as Russian trolls: Jenna Abrams and Pamela Moore were followed by tens of thousands, including members of Trump’s campaign.” The Philadelphia Inquirer. November 3, 2017. http://www.philly.com/philly/news/politics/presidential/russia-fake-twitter-facebook-posts-accounts-trump-election-jenna-abrams-20171103.html.
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U.S. House of Representatives, Permanent Select Committee on Intelligence (2017). Social Media. https://democrats-intelligence.house.gov/facebook-ads/social-media-advertisements.htm
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Wineburg, Sam, Sarah McGrew, Joel Breakstone, & Teresa Ortega. “Evaluating Information: The Cornerstone of Civic Online Reasoning.” Stanford Digital Repository. 2016. https://purl.stanford.edu/fv751yt5934.
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 See Noelle-Neumann (1974) for an explication of “spiral of silence” theory.
 See U.S. House of Representatives, Permanent Select Committee on Intelligence (2017) database for examples of these.