Political trolling on social media platforms is more common than ever before, attracting media and scholarly attention. We examined if trolling target’s gender and ideology impact the extent of trolling towards their tweet, based on content analysis of 3,000 Twitter comments. We found both main and interaction effects of gender and political affiliation on trolling, specifically: tweets by female politicians were trolled more than tweets by male; tweets by Republicans were trolled more than tweets by Democrats; and tweets by male Democrats were trolled less than tweets by all other politicians.
Findings and discussion
In June 2020, President Trump tweeted a video titled “terrified toddler runs from racist baby” that ended up being a manipulated CNN video (Spring, 2020). This was not the first time President Trump has run into troubles from his Twitter use. In 2019, the United States Court of Appeals for the Second Circuit unanimously ruled that the President cannot block Twitter users from his feed because he uses the account to inform American citizens on government-related issues (Savage, 2019). Some scholars argue that this ruling is unconstitutional and harmful because it could potentially discourage government officials from participating in social media (Beausoleil, 2019). This comment is paradoxical and interesting because conservative politicians around the world, specifically in Turkey, use Twitter as a platform to do exactly that; silence opposing views and discourage political discussions on social media (Karatas and Saka, 2017). In Turkey, conservative party members use a two-groups tactic to troll; the first group being closely related to the president (presidential advisors and conservative journalists), and the second being anonymous right-leaning users who follow the president’s lead (Karatas and Saka, 2017). This tactic has led to online lynching, self-censorship, and the abandonment of social media for some victims which in turn inhibits citizens from having political discussions (Karatas and Saka, 2017). President Trump’s frequent Twitter use has led to an increase in political trolling and media-related attacks by and toward him. This comes at a time that social media giants like Twitter and Facebook have been receiving backlash for years over their lack of interference when it comes to trolls, hate speech, fake news, and bots roaming their sites. Recently, however, Twitter was the first major social networking platform to take a stance on misinformation in America by labeling tweets as ‘manipulated media.’ Still, political trolling on social media platforms is more popular than ever before, and while trolling differs across platforms and communities, it is possible that it also varies due to the trolling target’s ideological inclinations and demographics rather than exclusively on the troll. Since ideological and political trolls are, by definition, ideologically driven, they may choose their targets accordingly. Thus, race, gender, ethnicity, age, political views, and country of origin of the target could affect trolling substantively. Politics in general involve more controversy than other topics, and women have been frequently targeted by online trolling, but it is unclear if particular political affiliations impact the extent of trolling, and if there is any interaction effect of gender by political affiliation on trolling. Women with particular political affiliations may be the target of trolling more frequently than others. We designed a 2-by-2 factorial study to examine the impact of target gender and political affiliation on the extent of trolling on Twitter, posing the following research question: Do trolling targets’ political party affiliation and gender impact trolling behaviors and tactics?
Research into the relationship between trolling and gender is limited. However, some scholars reported on trolls targeting feminists (Hardaker and McGlashan, 2016; Herring, et al., 2002), while others found that the troll’s gender affects the perception of and reaction to trolling, attributing different motives and behaviors to male and female trolls (Fichman and Sanfilippo, 2015; Todd and Melancon, 2019). In fact, trolling is more frequently done by males, and when women do troll, their motivations are seen to be more ideological rather than malicious (Fichman and Sanfilippo, 2015). Further, females are more likely to be the target of online trolling and cyber-harassment than males (Backe, et al., 2018). Akhtar and Morrison (2019), for example, discovered that female U.K. Parliament members were more likely to face racial and sexual abuse paired with misogynistic comments whereas male U.K. parliament members faced more abuse based on their professional duties and actions (Akhtar and Morrison, 2019). This led female trolling victims to be concerned for their personal safety and male trolling victims to be concerned about damage to their reputation (Akhtar and Morrison, 2019). Abusers often use gender, sexuality, and ethnicity to devalue an opponent’s ideas and/or beliefs, so trolls may target females more than males because they feel they can use the victims’ gender as a weapon against them in a debate. Scholars found disproportionate harassment toward minority women on Twitter in comparison to white women (Blum, 2018). Tweets toward women were abusive (both physically and sexually), and women who were politically associated with the left were targeted more than others (Blum, 2018). This online harassment has pushed some women to leave the platform (Blum, 2018) or has pushed women to not say what they truly want because they simply do not want to deal with the consequences (Feiner, 2019). Thus, we propose to test the following hypothesis:
H1: Tweets by female politicians will be trolled more than tweets by male politicians
Furthermore, politics in general seems to attract more trolling than other topics of discussion (Guy and Shapira, 2019) and to have a strong negative sentiment (Kucuktunc, et al., 2012); in fact, politics was rated as the subject most likely to provoke trolling behavior (Gammon, 2014). Politicians have been utilizing social media, and Twitter in particular, to promote their political agendas (Parmelee and Bichard, 2012) all over the world. Scholars have investigated social media use in India (Rajput, 2014), Canada (Small, 2011), or Turkey (Gökçe, et al.,2014) to name a few, and others compared social media use by leaders across several countries (Aharony, 2012). While some political leaders have been criticized for their social media use, and President Trump was even called a troll (Bauman, 2020), most other politicians have been trolled. Most American politicians have been trolled by the public (Sanfilippo, et al., 2017), and many politicians have been the target of state-sponsored trolling both within their country and from other countries (Nyst and Monaco, 2018). The Russian interference in the 2016 U.S. presidential election, using troll bots (Linvill, et al., 2019), is an example that has attracted significant attention recently. Interestingly, prior to the 2016 U.S. presidential election, the IRA focused on divergent and often contrary agendas, equally supporting both right-wing and left-wing ideologies in their disinformation agendas in an effort to divide the country along partisan lines (Linvill and Warren, 2020). Other examples involve state-sponsored trolling of dissenting views by left-wing opponents within their own country, with known cases from Azerbaijan, Bahrain, Ecuador, the Philippines, United States, and Venezuela (Nyst and Monaco, 2018), as well as Turkey (Bulut and Yuruk, 2017). In fact, some politicians are more likely to be the target of online trolling and harassment than others, in no small part because of their political affiliation. While trolling on both sides of the political spectrum is common, there are more anecdotes about left-wing targets being trolled by right-wing trolls than the other way around (e.g., Bauman, 2020; Burroughs, 2013; Shane and Blinder, 2019). Thus, we propose to test the following hypothesis:
H2: Tweets by Democratic politicians will be trolled more than tweets by Republican politicians.
Finally, because we expect that female politicians will be trolled more than male politicians and that left-wing politicians will be trolled more than right-wing politicians, we can expect that female Democrats will be trolled the most and male Republicans will be trolled the least. In fact, women who were politically associated with the left were targeted more than others (Blum, 2018). Thus, we propose to examine the interaction effect of gender by political affiliation and propose to test the following hypothesis:
H3: Tweets by female Democratic politicians will be trolled more than all other politicians.
Online trolling occurs when someone deliberately posts provocative, irrelevant, or offensive comments to antagonize others. There are various reasons why people feel like they can troll by posting online even if it is not something they would generally say to someone face-to-face. A few of these reasons include anonymity, perceived invulnerability, and lack of consequences. These factors enable trolling that is driven by motives such as boredom, attention seeking, entertainment, or malevolence. There are also other reasons to promote a specific ideology or to troll for activism purposes. This leads to the various types of trolling such as political, ideological, and satirical. Political trolling is trolling for a political purpose for example to call like-minded people to action over a political matter (Saviaga, 2019). Ideological trolling is used to promote certain values, often free speech, and to demarcate the boundaries of proper debate (Birkbak, 2018; Sanfilippo, et al., 2017). Satirical trolling focuses more on irony and provoking people into “expressing easily challenged positions, then exhausting them through mockery. ...” (Aspray, 2019). It also focuses more on the “lulz” aspect of online trolling. However, this type of trolling can still be harmful because targets of organized trolling can experience depression, anxiety, low self-esteem, and frustration (Saviaga, 2019). These effects are rather similar to those of cyber-bullying. It is also potentially harmful because even if people adopt extremist ideological ideas for laughs, “it can have the same impact of promoting these ideas into wider circulation” (Aspray, 2019).
Recently, social media platforms have been “politically weaponized” through the use of political trolling (Flores-Saviaga, et al., 2019). While often trolls are in it for fun, “political trolls are usually in it for ideological reasons ... [they often bait their] ideological opponents into arguments or [coordinate] ‘civic spamming’ campaigns” . By using provocative language, trolls inspire and call other online users to arms and to join their cause. These political trolls typically mock political leaders or anyone who does not hold similar beliefs to their own, by creating hashtags and spamming social media accounts of their opponents. Flores-Saviaga, et al. (2019), who studied trolling by analyzing the subreddit /r/The Donald (T D), identified three styles that these right-wing political trolls have used to mobilize their community before the 2016 election. Trolls used the “Historian” style to explain in detail why and how the community believes the things it does. The “Troll Slang” style used pro-Trump vocabulary and conspiracy theories; the conspiracy theories were invented to refute stories that challenged official statements by re-framing events in a way that discredited their opposition. The third style was “Viral News” which involves short, sweet, and to the point posts, so that readers did not have to spend time or energy reading posts. Similar to political trolling, ideological trolls are motivated by a set of values, and they often justify their posts as “freedom of speech”. Ideological trolling “often reproduces rather than disturbs existing political categories” (Birkbak, 2018). Just like political views, off-line ideological sentiments can even be reproduced online and vice versa (Evolvi, 2018). At times, ideological and political trolling use satire to bait their targets (Sanfilippo, et al., 2017).
As trolling spread widely online, it became common also on Twitter, which is a microblogging platform that has operated since 2006. Over time, Twitter has done little to limit online harassment in the U.S.; therefore, individuals being attacked have little recourse if they want to continue to use the platform. Some people attempt to limit the harassment by blocking the troll’s account, but some trolls will target multiple people at a time and tag several handles making it difficult for users to block the hate speech without Twitter eliminating the tweet in its entirety (Feiner, 2019). Until recently, Twitter seems unconcerned about this issue because despite the amount of harassment, many users claim the benefits of Twitter outweigh the negative aspects (Feiner, 2019). However, Twitter functions slightly differently around the world. For example, in Germany, Twitter is forced to comply with stricter laws than in the U.S., and for that reason, Twitter is much better at blocking unpleasant posts, such as swastikas (Feiner, 2019). Some non-German citizens even change their location to Germany to avoid pro-Nazi content because Twitter manages to keep the site clean (Feiner, 2019). In May, 2018, “American” Twitter announced it would start to hide trolls’ remarks by using algorithms to find suspicious behaviors (Roettgers, 2018). Twitter claimed, “Less than 1% of accounts make up the majority of accounts reported for abuse, but a lot of what’s reported does not violate our rules” (Roettgers, 2018). In fact, the word troll is not even mentioned in Twitter’s Terms & Agreement Section (Abril, 2016). This makes it difficult to actually stop trolling and harassment. Some people claim that Twitter’s lack of regulation of trolls and harassment affects the online community because diverse voices are being left out (Blum, 2018; Feiner, 2019), especially those of women and minorities.
Besides disproportionately hurting women, there are claims that trolling on Twitter is actually hurting democracy (Theocharis, et al., 2016). At the beginning, people were excited about the effects social media would have on democracy, bringing politicians and their constituents closer together (Theocharis, et al., 2016). However, a Washington Post survey in 2016 found that politicians were actively avoiding citizens on social media. While citizens bear some of the blame for the previous claim, trolling is discouraging democracy because it “may downgrade the overall quality of online discussions, demobilize citizens who are trolled and reduce satisfaction with the platform’s potential for discourse” (Theocharis, et al., 2016). In any case, trolling on Twitter is widespread and some targets may face it more frequently than others. Thus, our study focuses on the impact of the political affiliation and gender of targets on trolling.
In an effort to address our research question that aimed at identifying if, and to what extent, do trolling targets’ political party affiliation and gender impact trolling behaviors, we planned a 2-by-2 factorial design with Twitter data.
Using ExportComments.com, we collected data from Twitter on 9–16 February 2020, through an account we created for that purpose. A total of 200 tweets, from eight Twitter accounts of politicians, four Republican and four Democrats, four men and four women. We collected the most recent 25 tweets per politician account, and all the available respective comments. Tweets from most of the accounts were posted on February 2020, some were posted as early as December 2019, and in one account most of the tweets were posted during 2019. The specific politicians’ accounts per each of the four conditions in our design are listed in Table 1. We uploaded all the tweets and their respective comments into Nvivo11, a software for qualitative data analysis by QSR, to facilitate content analysis.
Table 1: List of Twitter account holders per condition. Democrats Republicans Female Nancy Pelosi (Speaker of the U.S. House); Alexandria Ocasio-Cortez (U.S. House of Representatives) Elise Stefanik (U.S. House of Representatives); Liz Cheney (U.S. House of Representatives) Male President Obama; Conor Lamb (U.S. House of Representatives) President Trump; Paul Ryan (Past Speaker of the U.S. House)
We developed a codebook of trolling behaviors and tested it on a sample of our data, and through an iterative process of discussion among the authors we combined, removed, or added codes, until we finalized our codebook (Table 2). We randomly selected 3,000 posts for further analysis; from each of the 200 tweets we selected the first 15 comments. Then, using Nvivo, we coded the data at the individual post level; each post was coded with all the relevant codes. One coder coded the entire dataset, and we calculated inter-coder reliability using other tweets and comments. Inter-coder reliability was 77.5 percent, which is an acceptable level for simple agreement. Once the data was coded, we created frequency tables, and utilized the Nvivo matrix search feature to identify instances of codes by category. Then, using SPSS 26 we tested the significance of differences across gender, political affiliation, and the interaction effect.
Table 2: Codebook. Code Definition Example of comments on politicians’ tweets Derailment Purposefully leading a conversation off track. Coronavirus hasn’t affected our amazing job growth! So many jobs added! I will help our economy grow more than ever before! Hyperbole Exaggerating one’s strengths or another’s weaknesses. Their measly job experience makes them weak! Ideological misalignment Comments/remarks made because of a difference in political opinions. That is WRONG! You make up statistics because the truth hurts you too much. Ideological extreme language Contains extremist language used to critically describe a subject or their behaviors (e.g., an implicit slur). You liberal Nazi snowflake! Insulting Statement meant to insult an individual or group of people.
Includes swearing (using vulgar language, usually to elicit a reaction), mocking and name-calling (referring to a subject with negative words that make them appear foolish or dangerous).
You are such an idiot! Personal attacks Statement meant to target an individual. Includes character assassination (attempt to damage someone’s reputation by slander or misrepresentation of their views/behaviors). You are not a good actor; you are a fraud and a cheat. Provocation Statement intended to elicit a specific reaction. You don’t know how to do your job; you are a disgrace! Pseudo Sincere Providing the impression of particular motivations while actually being driven by other motivations.
Saying one thing to show they are fulfilling a campaign promise in order to get re-elected.
Findings and discussion
We found that tweets by all eight politicians were the target of trolling in comments, mostly through provocation and ideological misalignment, but also by insulting, personal attacks, derailment, and pseudo-sincere comments (Table 3).
Table 3: Frequency of trolling. Total Derailment 566 Hyperbole 21 Ideological misalignment 1,041 Ideological extreme language 90 Insulting 612 Personal attacks 436 Provocation 1,704 Pseudo Sincere 480 Total 4,950
As can be seen in Figure 1, there are noticeable differences across gender and political affiliation. We found that tweets by female politicians were the target of trolling more frequently than tweets by male politicians (Table 4); these differences were statistically significant (Table 5), supporting our first hypothesis (Table 6). These findings support prior research that found females are disproportionately the target of cyberviolence (Backe, et al., 2018) in general, and trolling in particular (Herring, et al., 2002). Future research may examine the impact of a trolling targets gender that are not politicians on the extent and type of trolling; the impact of other demographical dimensions, such as race, ethnicity, nationality, and age, of trolling target, on the extent and type of trolling behaviors can shed light on the relationships between trolling and gender.
Figure 1: Trolling frequency by political affiliation and gender.
Table 4: Frequency of trolling by gender and political affiliation. Democrat Republican Total Female 1,684 1,030 2,714 Male 586 1,650 2,236 Total 2,270 2,680 4,950
Table 5: Differences in the extent of trolling by gender and political affiliation. χ2 p df Gender by political affiliation 330.807 0.000 3 Gender 23.13 0.000 1 Political affiliation 17 0.000 1
Table 6: Summary of hypotheses testing results. Hypothesis Results H1: Tweets by female politicians will be trolled more than tweets by male politicians. Supported H2: Tweets by Democratic politicians will be trolled more than tweets by Republican politicians. Not supported H3: Tweets by female Democratic politicians will be trolled more than tweets by all other politicians. Not supported
We found that tweets by Republican politicians were the target of trolling significantly more frequently than tweets by Democratic politicians (Table 4), and that these were statistically significant (Table 5); thus, our second hypothesis was not supported (Table 6). We found support for the impact of the target’s political affiliation on trolling, but unlike our hypothesis, we found that tweets by Republicans were trolled more often than tweets by Democrats. First, this might be explained by the fact that at the time of data collection, the Republican party was in power, and therefore attracted more trolling toward their tweets. Another possible explanation is that the inclusion of Trump, in our sample, accounts for this bias. Wanless and Berk (2017) suggest “[to] many, Trump is a troll,” and Bauman  concludes that “it is unlikely that Trump meets the criteria [of the troll’s definition]. However ... his behavior on social media ... consists of a considerable amount of trolling.” Regardless of whether Trump is a troll or not, there is agreement that his online behaviors resemble trolling, which in turn leads to more trolling towards him. The third possible explanation is that it is a result of the fact that Twitter users in the U.S. are younger and more likely to be Democrats, according to the Pew Internet Research report (Wojcik, and Hughes, 2019). In fact, 80 percent of Twitter users are affluent millennials, according to Omnicore (2020), and Blank (2016) argues that research that relies on Twitter cannot be generalized as social science because of its digital divide. While our findings are not supportive of much of the existing literature, there are other examples of left-wing trolling towards right-wing politicians (Shane and Blinder, 2019). A fourth possible explanation may draw on the use of satire in trolling (Sanfillipo, et al., 2017), as political satire commonly demonstrates left-wing usage of ideological humor toward the right wing (Gal, 2018). However, because our analysis did not account for satirical trolling behaviors, this may not be a plausible explanation in the context of our study. Future research may try to uncover which of the above (or other) speculative explanations can be empirically supported.
Interestingly, we found that male Democrats were trolled significantly less than any of the other targets (Table 5). In fact, we expected to find that tweets by female Democrats would be trolled more often, but the differences between male Republicans and female Democrats in our dataset were negligible. Thus, our third hypothesis was not supported (Table 6). Since H2 was not supported it was not surprising that H3 was not supported either. However, we found a significant impact of a target’s gender by political affiliation on the extent of trolling. Tweets by male Democrats were the least likely to be trolled. This was not surprising once we found more trolling of females and more trolling of Republicans. It is possible that choosing a random or bigger sample of politicians on both sides of the aisle will help support, refute, or further clarify the interaction effect. We conclude that the interaction effect of a target’s gender by political affiliation is evident in our data, but the direction of this impact calls for a more nuanced understanding in the future.
Thus, we conclude that there is a significant impact of target’s gender, political affiliation, and gender by political affiliation on the extent of trolling towards their tweets. However, we found support only for one of the three hypotheses (Table 6). Tweets by female politicians, regardless of political affiliation, were the target of more trolling than tweets by male politicians. Tweets by Republican politicians, regardless of their gender, were the target of more trolling than tweets by Democrat politicians. Tweets by male Democrats were the target of less trolling than any other group of politicians. We summarize the results of our hypotheses testing in Table 6.
While it is possible that our findings are biased because of the particular politicians we sampled, or the small sample size, we chose four accounts on each side of the political spectrum, and from each gender, to control for individual variations. It is also possible that data from another platform can shed additional light on the nature of the interaction effect. This may address the possible bias in the demographics of Twitter users; however, the role of Twitter in political communication is uncontested. Furthermore, while we made an effort to include a range of political positions, it is still possible that another competing variable, such as the target’s age or political role, is a factor here.
Political trolling on social media platforms is widespread (Bauman, 2020). While much of the literature on trolling focuses on the trolls’ motivations and actions, we addressed the need for a better understanding of the impact of trolling target attributes on trolling behaviors. We examined the impact of a target’s gender (female/male) and political affiliation (Democrats/Republicans) on the extent of trolling towards their tweets. Our findings show a significant impact of a target’s gender and political affiliation on the extent of trolling toward their tweets. We found a significant effect of gender, a significant effect of political affiliation, and a significant interaction effect of gender by political affiliation on the extent of trolling towards a target’s tweets. As expected, we found that female tweets have been trolled more frequently, but contrary to our expectations, we found that tweets by Republicans have been trolled more than tweets by Democrats, and that tweets by male Democrats were the least trolled. The findings about the impact of a targets gender on the extent of trolling support existing research, but the unexpected findings show that Republicans’ tweets, rather than Democrat’s tweets are trolled more frequently. This suggests a need for future research to compare trolling across the two sides of the aisle, perhaps using data from another sociotechnical platform, rather than Twitter.
As trolling and misinformation on social media are fast increasing the major contributions of our study are timely and important. This study provides a nuanced analysis of user behaviors, with the intended and unintended consequences of socio-technical platforms, such as Twitter. Trolling on social media is frequently gendered and ideologically driven, with significant implications for politicians and major impacts on citizens’ political views and behaviors; online trolling is woven into the fabric of American democracy.
About the authors
Pnina Fichman (Ph.D., University of North Carolina, Chapel Hill) is a professor of information science at the Luddy School of Informatics Computing and Engineering, and the director of the Rob Kling Center for Social Informatics at Indiana University, Bloomington. Her research interests include social informatics, information quality, technology and diversity, online communities and virtual teams, and online trolling. Besides the five books she co-authored or co-edited, her publications have appeared in journals such as I&M, IC&S, JASIS&T, JIS, and TIS.
E-mail: fichman [at] indiana [dot] edu
Maren W. McClelland is a student at Indiana University, Bloomington. Her research interests include social informatics, cultural differences, global development, and online trolling.
E-mail: marwmccl [at] iu [dot] edu
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Received 10 August 2020; revised 30 November 2020; accepted 1 December 2020.
Copyright © 2021, Pnina Fichman and Maren W. McClelland. All Rights Reserved.
The impact of gender and political affiliation on trolling
by Pnina Fichman and Maren W. McClelland.
First Monday, Volume 26, Number 1 - 4 January 2021