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Qanon and Violent Rhetoric on Twitter Locus: The Seton Hall Journal of Undergraduate Research Volume 3 Article 11 October 2020 Where We Go One, We Go All: QAnon and Violent Rhetoric on Twitter Samuel Planck Follow this and additional works at: https://scholarship.shu.edu/locus Recommended Citation Planck, Samuel (2020) "Where We Go One, We Go All: QAnon and Violent Rhetoric on Twitter," Locus: The Seton Hall Journal of Undergraduate Research: Vol. 3 , Article 11. Available at: https://scholarship.shu.edu/locus/vol3/iss1/11 Planck: Where We Go One, We Go All Where We Go One, We Go All: QAnon and Violent Rhetoric on Twitter Samuel Planck Seton Hall University Abstract Hillary Clinton or Barack Obama. The move- ment was one of many listed as part of an FBI in- This study concerns the rhetoric of the QAnon telligence bulletin concerning conspiracy theory- conspiracy theory as it appears on Twitter, and related violence (Fringe Political Conspiracy The- compares that rhetoric to that of mainstream con- ories). Followers of the theory have commit- servatives on the same platform. By coding indi- ted several violent crimes: from murder attempts, vidual tweets’ content for specific instances of vi- both successful (Watkins) and failed (Haag and olent, religious, economic, or paranoid rhetoric, Salam), to trespassing, to vandalism (McIntire and and comparing samples of both of these popu- Roose). How does the rhetoric of these move- lations, the study aims to determine what differ- ments on social media sites, for instance Twit- ences there are between the QAnon community’s ter, differ from the conservative movement as a uses of rhetoric, particularly violent rhetoric, and whole? What specific topics do these conspiracy that of the mainstream conservative community. theorists concern themselves with? The results demonstrate that the QAnon move- This paper will seek to explore these questions ment is more likely to use violent terminology in by conducting an analysis of QAnon Twitter data their tweets, but is not able to find strong correla- and comparing it to a sample of mainstream con- tions between violent rhetoric and other forms of servative tweets. The author will then perform speech, such as paranoia or religiosity. Likewise, content analysis on both samples, to determine the QAnon movement frequently focuses on ritual- where the QAnon theory fits into the Conservative istic cults or on accusations of satanism, but not Twitter landscape. Principally, the paper seeks to in sufficient numbers to demonstrate a correlation prove that the amount of paranoia in a tweet is pre- between such accusations and violence. dictive of the amount of violence that tweet will display. 1. Introduction 2. Literature Review The far-right movement known as QAnon has The first issue debated by scholars is that of become an increasingly potent phenomenon on collection methodology. Most scholarship falls social media, and in real life. Dedicated to a con- into three categories in this regard. The first two spiracy theory centered around President Donald concern networks and clusters of individual ac- Trump and a mysterious figure in the administra- counts, attempting to map relationships so that re- tion known as ‘Q’, the theory began on anony- searchers may better understand the greater pic- mous messaging board 4Chan in 2017, offering tures of how individual accounts interact with explanations for the lack of arrests of figures like each other. The two types I have identified for Published by eRepository @ Seton Hall, 2020 1 Locus: The Seton Hall Journal of Undergraduate Research, Vol. 3, Iss. 1 [2020], Art. 11 these purposes are follow-based or activity-based. ical Challenges 2.0” presents possible solutions. The first is demonstrated in the paper “The Aus- Specifically, the paper discusses ‘Traces’ (Cros- tralian Twittersphere in 2016: Mapping the Fol- set et al. 941). By performing basic algorithmic lower/Followee Network”. The authors proposed content analysis, accounts can be identified related to identify Twitter accounts from a massive sam- to the alt-right. Particularly, the paper identifies ple of “accounts which meet any one of a number that the far-right hides behind a great deal of eu- of criteria for ‘Australianness,’ rather than snow- phemism and irony in order to recruit and spread balling out from a set” (Bruns et al. 3). Using the propaganda. The methodology, particularly iden- application program interface (API) provided by tifying euphemisms frequently used by the far- Twitter, the team measured the outward connec- right, will be useful in my own research, since tions of a sample of the Australian accounts. By groups like QAnon have a very specific vocabu- coding these accounts and grouping them based lary that they use. on common interests through an algorithm, the re- “The Alt-Right Twitter Census” by J. M. searchers were able to place them in communities Berger presents a number of methodological tech- based on topic and follower clusters. This research niques that will certainly prove useful in current demonstrates a very clear cluster effect on Twit- research. The goal of the paper was to conduct ter, what many call an ‘echo chamber’ when relat- a simple data gathering of the Twitter data of the ing to politics especially. On the other hand, one Alt-Right, and their associated mainstream right can attempt to use an Activity-based methodology counterparts. The report identifies several cate- for measuring communities, as in the paper “In- gories into which Alt-Right Twitter accounts fall, fluential Spreaders in the Political Twitter Sphere based on their principle issue: immigration, sup- of the 2013 Malaysian General Election”. This port for Trump, white nationalism, or general ha- process begins with clustering accounts in opinion rassment. These groups came into occasional con- communities, but takes the further step of measur- flict with one another. The research analyzed the ing interactions (likes, retweets) in order to deter- 200 most recent tweets from more than 29,000 ac- mine which accounts are the most influential in counts. The accounts were identified through the their given communities (Hong-liang et al. 57-58). Follower-Followee system of the Australian Twit- This is particularly effective for short-term analy- ter Landscape paper, creating a map of Alt-Right sis, as in this paper, which collected data over two groups and their interactions with one another. non-consecutive weeks, to study the relationship They then created a control group of approxi- between tweets and election data. Given this in- mately 30,000 accounts. The researchers gath- formation regarding temporal effectiveness (long- ered data concerning account biographies, text in term vs. short term data analysis), it may be better tweets, and hashtag use. The goal of the paper to use the short-term analysis to make up for the was quantitative, simply to measure the overall short amount of time available to conduct this re- presence of the Alt-Right online and to survey search. which topics they discussed, where this study will These papers have useful general principles be qualitative. While this research does allow for for Twitter research, but concern unrelated polit- interesting collection techniques, content analysis ical issues. The far-right, specifically on Twit- will require more investigation. ter, and specifically within the United States, may have sufficiently different characteristics to 3. Research Design Malaysian General Election networks or Aus- tralian society as a whole. The article “Research- First, we must discuss the possibilities for col- ing Far Right Groups on Twitter: Methodolog- lecting the data on Twitter. However, using the https://scholarship.shu.edu/locus/vol3/iss1/11 2 Planck: Where We Go One, We Go All Twitter search function in and of itself is not suf- while not being a figurehead around which con- ficient to the task, since Twitter is not a random spiracy theorists or alt-right figures congregate. site, but influenced by thought leaders of clusters Specifically, we will be looking for violent or of people. This Network-based analysis is what dehumanizing rhetoric, searching for things such is used in most examinations of Twitter data. In as referring to groups of people (immigrants, Mus- order to judge the feelings of the community, we lims, LGBT people) as insects, vermin, plague, give weight to the most popular figureheads in the or the like. This dehumanizing language is a movement, accounts such @prayingmedic, an es- primer for violence against such groups (Luna tablished leader of the conspiracy theory with over 253). Likewise, we will code for references to 273,000 followers, and at least one retweet by the religion and to the economy, and consider senti- President himself, on 21 February, 2020 (@pray- ment more generally: whether the tweets on the ingmedic). By analyzing the accounts of accepted whole express positive or negative emotions. This and influential leaders in the community, we can gives us more than one factor with which to com- minimize the amount of non-real Twitter accounts pare the QAnon movement with mainstream con- entering into our sample and jeopardizing the va- servatives. Do they focus more on violent rhetoric, lidity of the sample. while conservatives focus on economic policy and @Prayingmedic will act as our ‘leader ac- news more generally? By comparing these mul- count’, the nexus of our network. Having identi- tiple categories, we can better determine diver- fied a leader, we take a sample from their follower gences between the two. base, equal
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