"Is it a Qoincidence?": An Exploratory Study of QAnon on Voat
Antonis Papasavva, Jeremy Blackburn, Gianluca Stringhini, Savvas Zannettou, Emiliano De Cristofaro
““Is it a Qoincidence?”: A First Step Towards Understanding andCharacterizing the QAnon Movement on Voat.co
Antonis Papasavva , Jeremy Blackburn Gianluca Stringhini , Savvas Zannettou ,and Emiliano De Cristofaro University College London, Binghamton University, Boston University, Max Planck Institute for [email protected], [email protected], [email protected], [email protected], [email protected]– iDRAMA Lab –
Abstract
Online fringe communities offer fertile grounds for users toseek and share paranoid ideas fueling suspicion of mainstreamnews, and outright conspiracy theories. Among these, theQAnon conspiracy theory has emerged in 2017 on 4chan,broadly supporting the idea that powerful politicians, aristo-crats, and celebrities are closely engaged in a global pedophilering. At the same time, governments are thought to be con-trolled by “puppet masters,” as democratically elected offi-cials serve as a fake showroom of democracy.In this paper, we provide an empirical exploratory anal-ysis of the QAnon community on Voat.co, a Reddit-esquenews aggregator, which has recently captured the interestof the press for its toxicity and for providing a platform toQAnon followers. More precisely, we analyze a large datasetfrom /v/GreatAwakening, the most popular QAnon-relatedsubverse (the Voat equivalent of a subreddit) to character-ize activity and user engagement. To further understand thediscourse around QAnon, we study the most popular namedentities mentioned in the posts, along with the most promi-nent topics of discussion, which focus on US politics, DonaldTrump, and world events. We also use word2vec models toidentify narratives around QAnon-specific keywords, and ourgraph visualization shows that some of QAnon-related onesare closely related to those from the Pizzagate conspiracy the-ory and “drops” by “Q.” Finally, we analyze content toxicity,finding that discussions on /v/GreatAwakening are less toxicthan in the broad Voat community.
Broadly speaking, conspiracy theories typically credit se-cret organizations or cabals for controversial, world-changingevents, while rejecting explanations given by officials [22]. Inmany cases, conspiracies posit that important political eventsor economic and social trends are the product of deceptiveplots mostly unknown to the general public. A prominent ex-ample relates to the disappearance of Malaysia Airlines FlightMH370, which is alleged to have been taken over by hijackersand flown to Antarctica [44].The ability to find like-minded people, at scale, on social media platforms has helped the spread of conspiracy theo-ries in general, and politically oriented ones in particular.For instance, the Pizzagate conspiracy theory emerged duringthe 2016 US presidential elections, claiming that candidateHillary Clinton was involved in a pedophile ring [40]. Evenwhen widely debunked, conspiracy theories can help motivatedetractors and demotivate supporters, thus potentially threat-ening democracies.Over the past few years, a new conspiracy, known as“QAnon,” has emerged that is somewhat related to Pizzagate.It originated on the anonymous Politically Incorrect (/pol/)board of 4chan by a user going by the nickname “Q,” whoposted numerous threads claiming to be a US governmentofficial with a top-secret Q clearance, in October 2017 [10].They explained that Pizzagate is real and that many celebri-ties, aristocrats, and elected politicians are involved in thisvast, satanic pedophile ring. Q further claimed that PresidentDonald Trump is actively working against a cabal within theUS government trying to defeat his crusade. QAnon incor-porates many theories together into a broadly defined super-conspiracy theory . QAnon adherents also believe that manyworld events, including the COVID-19 pandemic, are partof a sinister plan orchestrated by “puppet masters” like BillGates [31]. Zuckerman [72] argues that QAnon supporterscreate a vast amount of material that eventually becomes vi-ral. For instance, the book “QAnon: An Invitation to a GreatAwakening” [70], written by QAnon followers, ranked secondon the Amazon best-selling books list [32].After Reddit banned many popular QAnon-related subred-dits in September 2018 [54, 46], QAnon followers reportedlymigrated to Voat.co. Voat is a news aggregator, structuredsimilarly to Reddit, where users can subscribe to differentchannels of interest, known as “subverses.” Newcomers arenot allowed to create new submissions, but they can upvote ordownvote submissions and comments, as well as being able tocreate comments on existing submissions. Once users manageto get a total of ten upvotes on their comments, they can createnew submissions to any subverse.As with many “fringe” platforms (e.g., Gab), Voat was de-signed and marketed vigorously around unconditional supportof freedom of speech against the alleged anti-liberal censor-ship perpetrated by mainstream platforms. A year after its1 a r X i v : . [ c s . C Y ] O c t reation, HostEurope.de stopped hosting Voat because of thecontent posted [4] and, shortly after, PayPal froze their ac-count [16]. In August 2015, Voat was thrust into the spot-light when Reddit banned various hateful subreddits (e.g.,/r/CoonTown and /r/fatpeoplehate [57, 55]) and a large num-ber of users reportedly migrated over [43, 2, 15]. Research Questions.
In this paper, we focus on the QAnon-focused community on Voat. More specifically, we set out toanswer the following research questions:
RQ1
What does activity by the QAnon movement on Voatlook like?
RQ2
Which words and topics are most prevalent for and bestdescribe the QAnon movement on Voat? What narrativeare shared and discussed by QAnon adherents?
RQ3
How toxic is content posted on QAnon subverses? Howdoes it compare to popular subverses focusing on generaldiscussion?
Methodology.
To address RQ1, we provide a tempo-ral analysis of the most popular QAnon-focused subverse,/v/GreatAwakening, in comparison to a baseline dataset,which includes the four most popular subverses (in termsof posting activity) focusing on general discussion: /v/news,/v/politics, /v/funny, and /v/AskVoat. We also analyze sub-mission engagement and user activity. Then, we detect pop-ular named entities, and use topic detection tools as wellas word embeddings along with graph representations ofQAnon-specific keywords in an attempt to define the narra-tives around the QAnon movement (RQ2). Finally, to studytoxicity within these communities (RQ3), we use Google’sPerspective API [48] to measure how toxic the posts in ourdataset are.
Main Findings.
Overall, our work provides a fist characteri-zation of the QAnon community on Voat, and more preciselyof /v/GreatAwakening. Among other things, we find that sub-verse to attract many more daily number of submissions thanthe four (popular) baseline subverses. Indeed, users tend tobe quite engaged, with two of the most active QAnon submit-ters creating over . of the submissions of the baselinesubverses as well. Also, we analyze user profile data and findthat over . (2.3K) unique users registered a new accounton Voat when Reddit banned QAnon subreddits in September2018.Then, using a word2vec model to illustrate words closelyrelated to QAnon-specific keywords, we show that the move-ment still discusses, among others, its predecessor Pizzagateconspiracy theory, the posts of the user Q, and other social me-dia. We also show that the most prominent topics of discus-sion are centered around the US, political matters, and worldevents, while the most popular named entity of the discussionis President Trump. Finally, we find that the QAnon commu-nity on /v/GreatAwakening is . less toxic than on base-line subverses. As discussed later in Section 3, we also identify 16 other subverses re-lated to QAnon but find them to be inactive, thus, we only focus on/v/GreatAwakening.
In this section, we discuss the history, origins, and beliefs ofthe QAnon movement. We also provide a high-level explana-tion of the main functionalities and features of Voat.
Origins.
QAnon originates from posts by an anonymous userwith the nickname Q. On October 28, 2017, Q posted a newthread with the title “Calm before the Storm” on 4chan’s Po-litically Incorrect board (/pol/). In that thread, and over manysubsequent cryptic posts, Q claimed to be a government in-sider with Q level security clearance. The user claimed tohave got their hands on documents related to, among otherthings, the struggle over power involving Donald Trump,Robert Mueller, the so-called “deep state,” and Hillary Clin-ton’s pedophile ring [69]. The deep state is believed to be asecret network of powerful and influential people (includingpoliticians, military officials, and others, that have infiltratedgovernmental entities, intelligence agencies, etc.), and thatallegedly controls policy and governments around the worldbehind the scenes, while officials elected via democratic pro-cesses are merely puppets. Q claims to be a combatant in anongoing war, actively participating in Donald Trump’s cru-sade against the deep state [56].
Ongoing activities.
Q has continued to drop “breadcrumbs”on 4chan and 8chan, giving birth to a community named afterthe nickname of the anonymous user: “QAnon.” The com-munity is devoted towards decoding the cryptic messages ofQ to figure out the real truth about the evil intentions of thedeep state, pedophile rings run by aristocrats, and updates onthe noble, multi-front war President Trump is waging. Al-though this movement was not initially very popular, mostlyconfined to a small group [69], it has since grown substan-tially via mainstream social networks like Facebook, Reddit,and Twitter. For example, QAnon adherents around the worldhave staged protests [14, 11], and there are at least 25 USCongressional candidates with direct links to QAnon who willappear on ballots during 2020 US Presidential Election [3].
Relevance.
Previous work studied the dangers and threatsconspiracy theories pose to democracies and the generalpublic. Specifically, Douglas and Sutton [23] explain howthe conspiracy theory surrounding the global warming phe-nomenon potentially threatens the whole world. The authorsnote that the uncertainty, fear, and denial of climate changecause people to seek other explanations. Alarmingly, climatechange conspiracy theories can be harmful as people who be-lieve them often deny to take environmentally friendly initia-tives. Therefore, governments and many environmental orga-nizations face significant challenges towards convincing peo-ple to take action against global warming.Sternisko et al. [64] and Schabes [61] argue that conspir-acy theories, including QAnon, are extremely dangerous fordemocracies. In fact, government officials and media often Q access authorization is the US Dept. of Energy equivalent to the USDept. of Defense top-secret clearance. and QAnon , during herfarewell address [62]. At a recent rally for Donald Trump, theperson that introduced Donald Trump used the QAnon motto“where we go one, we go all” to conclude his speech [37].With the 2020 U.S. Presidential Elections looming, the FBIhas recently described the QAnon movement as a domesticterror threat [37], and its followers as “domestic extremists.”
QAnon on social networks.
Mainstream social networkslike Reddit, Twitter, and Facebook are trying to ban QAnon-related groups and conversations. Specifically, Reddit wasthe first social network to ban numerous subreddits devotedto QAnon discussion in 2018 [19, 46, 65]. Then, Twitter putrestrictions on 150K user accounts and suspended over 7Kothers that promoted this conspiracy theory. Twitter also re-ported that they would stop recommending content linked toQAnon [9, 45]. Facebook recently announced they were ban-ning QAnon conspiracy theory content across all their prop-erties [7], with YouTube following shortly thereafter [33].
Voat is a news aggregator launched in April 2014, initially un-der the name “WhoaVerse” and renamed to Voat in December2014.
Main features.
Areas of interest, called “subverses,” groupposts on Voat. Similar to Reddit, users were able to regis-ter new subverses on Voat on demand but this functionalityhas been disabled since June 2020. When a user registers anew subverse, they become the owner of the subverse. Theowner of a subverse can delete it and nominate moderatorsand co-owners, who can then delete comments and submis-sions. Notably, Voat limits the number of subverses a usermay own or moderate to prevent a single user from gainingoutsized influence.Users can register on Voat using a username, a password,and an email (optional). They can subscribe to subverses ofinterest, see, vote, and comment on submissions, but are ineli-gible to post new submissions at this point. Voat users refer tothemselves as “goats,” due to the mascot of the platform thatresembles an angry goat.
Submissions.
Figure 1 depicts an example of a Voat sub-mission: (1) shows the submission, (2) and (5) are commentsmade under the submission, and (3) and (4) are child andgrandchild of comment (2), respectively. A user can createa new submission by posting a title and a description or shar-ing a link and a description. If sharing a link, the title of thesubmission (see “a” in Figure 1) becomes a hyperlink to thesource website. The source website also appears next to thetitle of the submission (see “b”), along with the username ofthe user that posted the submission (“c”). Note that some sub-verses allow users to post anonymously. Other users can thencomment on the submission (comment 2 and 5 in Figure 1),or comment on comments of other users (comments 3 and 4).Also, users can “upvote” or “downvote” the submission (“d”
Figure 1:
Example of a typical Voat submission. Post with number(1) shows a Voat submission, while posts (2) to (5) are comments. in the figure) or the comments of other users. Submissionsand comments may have a negative vote rating based on thevotes they receive from users.A user becomes eligible for posting new submissions onlyif their
Comment Contribution Points (CCP) is equal orgreater than ten. Upvotes a user receives are added towardsher CCP, while downvotes are subtracted. Note that users losetheir eligibility to post new submissions once their CCP fallsunder ten.
Ephemerality.
Each subverse has a limit of 500 active sub-missions at a time: up to 25 submissions in 20 pages (page 0to page 19). When a user creates a new submission on Voat,that submission appears first on page 0, i.e., the subverse’shome page. At the same time, the submission at the end ofpage 19, usually the one with the least recent comment, dis-appears. That submission is still reachable, but only if oneknows its direct link; it is archived and new comments cannotbe posted. Notably, when a submission gets a new comment,it is bumped to the top of page 0, no matter when the sub-mission was originally posted. However, it is not clear whensubmissions on Voat stop being bumped when they get newcomments.
This section presents our data collection methodology as wellas our dataset.
Subverses.
Our first step is to identify Voat subverses thatare related to the QAnon movement. To do so, we startfrom several articles from the popular press [26, 57, 54],which highlight how several subreddits banned from Redditre-emerged on Voat. This happens for QAnon-related subred-dits as well [19, 46, 65], thus, we search for subverses withsame and similar names as the banned subreddits. We iden-tify 17 subverses and, upon manual inspection, confirm thatthey are indeed devoted to QAnon-related discussions. How-ever, we find that 16 out of 17 are essentially inactive, withless than 800 total posts over a period of almost 5 months.Therefore, we focus on the most active QAnon subverse,/v/GreatAwakening.We also use the four most active subverses as a baselinedataset. More precisely, we select the top four, in terms of3osts, from the top-10 most subscribed subverses: /v/news,/v/politics, /v/funny, /v/AskVoat. In the rest of the paper, werefer to these four general-discussion subverses as the “base-line subverses.”
Crawling.
We start crawling the five subverses on May 28,2020, using Voat’s JSON API , and stop on October 10, 2020.Voat does not provide an online listing of the archived submis-sions that fall out of the 20 pages limit, but these submissionsare still reachable if one knows the direct link to it, i.e., thesubverse it was posted in, and the submission ID. A manualinspection of the submission IDs in our database indicates thatthe submission IDs are monotonically increasing, and thus itis technically possible to collect submissions that fall out ofthe 20 pages limit by using submission IDs smaller than theones we collect on the first day that our data collection infras-tructure started operating. If the submission ID does not existwithin the subverses we are interested in, the API will return a404, and thus we could indeed enumerate through all possiblesubmissions. That said, doing this would require us to makemillions of requests to the Voat API, the majority of whichwould be 404s placing undue load on their servers, and, ifwe followed the Voat API usage limits, it would take severalyears to enumerate through all the possible submissions.Hence, we use the following methodology to collect all thesubmissions’ comments, focusing only on data posted afterMay 28, 2020, inclusive. For each subverse, our crawler con-tinuously requests the submissions pages from 0 to 19, usingVoat’s API. For each submission, we obtain its submission ID,and query the Voat API again to collect the comments postedon that submission.Voat’s API returns only up to 25 comments at a time (akacomment segments) for a given submission. Next, we notethat Voat has a hierarchical, tree-like commenting system,similar to Reddit, with some submissions resulting in branch-ing threads of varying depth. Thus, to ensure we collect allcomments on a submission, our crawler implements a depthfirst search (DFS) algorithm where we start with the com-ments returned by the first request to the API, and then it-eratively query for any child comments they might have. Foreach of the children discovered, we query for their children,until we fully explore the comment tree for the submission.The primary reason we went with a DFS implementation overbreadth first search (BFS) implementation is due to the VoatAPI returning comment segments : a DFS simply required abit less bookkeeping and is a more natural fit considering weare not guaranteed to get all comments at a given level witha single request. The crawler revisits the pages of every sub-verse, looking for new submissions, or updates on the onesalready collected, numerous times per day, ensuring the col-lection of the full state of submissions before they fall off thepage 19 limit. Dataset.
Table 1 lists the number of posts (submissionsand comments) we collect for each subverse analyzed in thisstudy. Our dataset spans posts from May 28 to October 10,2020. Note that our dataset is missing some posts between https://api.voat.co/swagger/index.html Subverse Posts Users /v/GreatAwakening 152,315 4,915/v/news 153,162 6,212/v/politics 107,214 5,610/v/funny 61,949 4,971/v/AskVoat 35,643 4,282
Total
Table 1:
Number of posts for each subverse in the dataset, alongwith the total number of user profiles collected.
June 9 and June 13 due to failure of our data collection infras-tructure.Besides submissions and comments, we also collect pub-licly accessible user profile data. More specifically, we col-lect profile data of the users posting a submission or a com-ment on /v/GreatAwakening and baseline subverses listed inTable 1. In total, we find 4.9K, 6.2K, 5.6K, 4.9K, and 4.2Kunique usernames that have either created a submission ormade a comment in /v/GreatAwakening, /v/news, /v/politics,/v/funny, and /v/AskVoat, respectively. The union of these re-sults in 15K unique usernames, with 13K of these usernameshaving accessible profiles. The remaining ∼
2K (13.16%) ofusernames we query result in a 404 error, which we believe isdue to profiles being deleted or deactivated.
Ethical considerations.
Note that we only collect openlyavailable data and follow standard ethical guidelines [51].Also, we do not attempt to identify users or link profiles acrossplatforms. Moreover, the collection of data analyzed in thisstudy does not violate Voat’s API Terms of Service.
In this section, we analyze aggregate and user-specific activ-ity, content engagement, and registrations for all subverses inour dataset.
We start by looking at how often submissions and com-ments are posted on the collected Voat subverses. Fig-ure 2(a) plots the number of daily submissions for the base-line and /v/GreatAwakening subverses (note log-scale on y-axis). From the figure, we see that, over a span of 4.5months, /v/GreatAwakening has more submissions than theindividual baseline subverses, with about 100 new submis-sions per day, on average. The next most active sub-verse is /v/news with about 70 new submissions per day.This is remarkable considering that, as of October 2020,/v/GreatAwakening has only 20K subscribers, while /v/newshas 100K. When looking at comment activity (Figure 2(b)),/v/news and /v/GreatAwakening are close, with 1.06K and1.01K comments per day, respectively.We observe a peak in submission and comment postingactivity on /v/GreatAwakening between June 29 and July 34 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
10 100 o f s u b m i ss i o n s GreatAwakening news politics AskVoat funny (a) Submissions - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
100 1 k o f c o mm e n t s GreatAwakening news politics AskVoat funny (b) Comments
Figure 2:
Number of submissions and comments posted per day in baseline subverses and in /v/GreatAwakening. C D F GreatAwakeningBaseline (a) Comments C D F QupvotesBupvotesQdownvotesBdownvotesQnetBnet (b) Votes C D F QupvotesBupvotesQdownvotesBdownvotesQnetBnet (c) Votes
Figure 3:
CDF of the number of (a) comments and (b) votes per submission on /v/GreatAwakening (Q) and baseline subverses (B), andnumber of (c) votes per comment on /v/GreatAwakening (Q) and baseline subverses (B). with the most submissions on July 2 (185 submissions and al-most 1.9K comments). Manual inspection indicates the peakin submission activity may be related to Jeffrey Epstein’s ex-girlfriend, Ghislaine Maxwell, being arrested by the FBI [8].Another peak in submission and comment posting activity ap-pears between August 10 and August 21 with a peak of 183submission on August 19. (Manual inspection does not re-veal any clear link to a specific event). Finally, October 7 hasthe most submissions on /v/GreatAwakening for a single day(207), which we believe is due to Facebook announcing theban of QAnon accounts, pages, and related content across alltheir platforms [7].
Next, we look at user engagement. Figure 3(a) plots theCumulative Distribution Function (CDF) of the number ofcomments per submission. On average, submissions on/v/GreatAwakening receive 10.4 comments, while the base-line subverses’ submissions get 16.2 comments. Specifically,Figure 3(a) shows that only . and . of the submis-sions on /v/GreatAwakening and baseline subverses, respec-tively, have more than 20 comments. The median numberof comments on /v/GreatAwakening submissions is 5 and onbaseline subverse’s submissions is 6, while the most popu-lar /v/GreatAwakening submission has 245 comments and themost popular baseline subverses’ submission has 403 com-ments. Our findings show that, although /v/GreatAwakeninghas the most submissions, the users of the baseline subversesare more engaged.Next, we look at how often users upvote and downvote sub- missions. We plot the CDF of upvotes, downvotes, and netvotes (e.g., upvotes - downvotes) the submissions get in Fig-ure 3(b). On average, /v/GreatAwakening gets 57.4 upvotesand 0.9 downvotes, while on baseline subverses we find 61upvotes and 1.5 downvotes. The most upvoted submission has537 and 870 upvotes on /v/GreatAwakening and baseline sub-verses, respectively, while the most disliked submission has37 downvotes on /v/GreatAwakening, and 114 downvotes inthe baseline subverses. Specifically, the title of the most up-voted /v/GreatAwakening submission is “The United States ofAmerica will be designating ANTIFA as a Terrorist Organiza-tion,” and the submission links to a tweet by President Trump.On average, the submissions of both /v/GreatAwakening andthe baseline subverses tend to have a net positive vote countin the end; about 48.8 for /v/GreatAwakening and 54.1 for thebaseline subverses.We observe that . and . of the/v/GreatAwakening and baseline submissions, respec-tively, have more than 20 upvotes. On the contrary, only . and . of the submissions on /v/GreatAwakeningand baseline subverses get more than 10 downvotes. Wealso run a two-sample Kolmogorov-Smirnov (KS) test on thedistributions of upvotes, downvotes, and net votes, and rejectthe null hypothesis ( p < . for all comparisons).Similarly, we plot the CDF of the number of upvotesand downvotes of comments in Figure 3(c). On aver-age, comments get 2.2 upvotes and 0.18 downvotes on/v/GreatAwakening. Comments of the baseline subverses get2.8 upvotes and 0.35 downvotes, on average. Again, we findstatistically significant differences between the distributionsvia the two-sample KS test ( p < . ).5 u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r A ll O t h e r s (a) /v/GreatAwakening submissions u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r A ll O t h e r s (b) /v/GreatAwakening comments u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r A ll O t h e r s (c) Baseline submissions u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r u s e r A ll O t h e r s (d) Baseline comments Figure 4:
Number of submissions and comments posted per user on/v/GreatAwakening and baseline subverses.
Overall, this shows that users of both communities tendto positively vote the content they encounter. Baseline sub-verses’ posts tend to be downvoted and upvoted more oftenwhen compared to the /v/GreatAwakening posts. This is prob-ably due to the great difference of audience between the twocommunities. Notably, both communities seem to be engag-ing towards commenting and voting the posts they encounterin the platform.
Next, we focus on user profile data to understand how oftenusers post new submissions on both /v/GreatAwakening andbaseline subverses. More specifically, we investigate whetherthe audience of /v/GreatAwakening and baseline subversesconsume information from specific users due to Voat’s rulenot allowing newcomers to post new submissions unless theyhave a CCP above 10.To do so, we count the number of submissions users postedon /v/GreatAwakening and the baseline subverses. We findthat the 13.5K submissions of /v/GreatAwakening were madeby 346 users. The 21.9K submissions of the baseline sub-verses were made by 1.8K users. Figure 4 reports the top15 submitters and commenters of both communities. To pro-tect users’ privacy, we replace the original usernames with“user1,” “user2,” etc.We observe that the top submitter, “user1” in Fig-ure 4(a), posted 22.9% (3.1K) of the total submissions on/v/GreatAwakening. Excluding the top 15 submitters, the re-maining 331 submitters (marked as “All Others” in the figure)are responsible for 28.2% (3.8K) of the submissions madeon /v/GreatAwakening. This is not the case for submissionsof general discussion as the top 15 submitters together areonly responsible for 26.8% (5.8K) of the total submissions,as depicted in Figure 4(c). Excluding the top 15 commenters,/v/GreatAwakening (Figure 4(b)) and baseline subverses (Fig-ure 4(d)) comment activity seems to fall on the broader au-dience of the communities since “All Others” post 80.9% / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / o f r e g i s t r a t i o n s (a) /v/GreatAwakening user registrations / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / o f r e g i s t r a t i o n s (b) Baseline subverses user registrations Figure 5:
Number of monthly user registrations of (a) users engag-ing on /v/GreatAwakening and (b) users engaging in all baseline sub-verses. (112K) and 92% (308.5K) of all the comments, respectively.Manual inspection of our dataset shows that . (3K)usernames overlap between /v/GreatAwakening and the base-line subverses. Namely, “user8” and “user9” are amongst thetop submitters of both communities, and “user30” ranks 1stcommenter in both. Our results suggest that the audience of/v/GreatAwakening (20K subscribers) consumes content andsubmissions from a handful of users (349 submitters), and toa great extent, from “user1.” We also analyze the registration dates of the users that postcontent in the two communities in an attempt to understandwhen these users registered a new account on Voat. Since2015, online press outlets have reported that communitiesbanned from Reddit often migrate to Voat [6, 46, 60]; thus,we investigate whether Voat user registrations increase whenReddit bans communities.We find that, during the period our data collection infras-tructure was active, over 15K users posted a submission or acomment on the subverses. Also, we find that 13.16% (2K)of these users deactivated their account, or their account wasdeleted by Voat, due to 404 errors our data collection infras-tructure received from Voat’s API.Figure 5(a) and Figure 5(b) plot the number of registeredusers engaged on /v/GreatAwakening and baseline subverses,respectively, per month. On /v/GreatAwakening, the averagemonthly registration is 4.1, 38.1, 22.75, 28, 125.9, 69.1, and75 for 2014, 2015, 2016, 2017, 2018, 2019, and 2020, respec-tively. Similarly, every month 8.6, 118.1, 50.9, 65.5, 179.6,142.1, and 200 new user registrations were made, on average,in the baseline subverses. Manual inspection of our datasetconfirms that over 17.6% (2.3K) unique users registered onVoat in September 2018 only, i.e., the month Reddit bannedmany QAnon-related subreddits [54, 46, 65]. We also ob-6 opic Words per topic for /v/GreatAwakening
Topic Words per topic for baseline subverses
Table 2:
LDA analysis of /v/GreatAwakening and baseline subverses. serve another spike in user registration in both communitiesbetween June and July 2015, probably due to Reddit banninga couple of hate-focused subreddits [57, 55, 52].Although our dataset might not be representative of Voat’suser base as a whole, it provides an indication of the datesusers decided to join the platform. Looking only at usersengaged in baseline subverses (Figure 5(b)), we confirm thatVoat received a high volume of new user registrations close tothe periods of Reddit banning hateful subreddits and QAnonrelated subreddits. Future work, in conjunction with Redditdata, might help shed more light on the effect of Reddit de-platforming and consequent user migration.
Overall, this section answers our RQ1, i.e., what does activityby the QAnon movement on Voat look like? The most popularQAnon-focused subverse, /v/GreatAwakening, attracts manymore submissions than the baseline subverses, despite theyare among the top 10 most popular subverses on the platformregarding subscriber count. Also, /v/GreatAwakening has al-ways more than 50 new submissions per day, with that num-ber steadily increasing over time and staying above 100 newsubmissions per day since September 25, 2020. Whereas, thenumber of daily submissions stays in the same margins for thebaseline subverses, except for /v/AskVoat, where we observea decline in posting activity.Moreover, we find that the audience of both communitiestend to commend and upvote the submissions and commentsthey see in the subverse. Also, it is clear that the audience of/v/GreatAwakening consumes information from just a hand-ful of users, while top submitters seem to overlap betweenthe QAnon-focused subverse and the baseline subverses. Fi-nally, we show that new user registrations peaked after Red-dit banned hateful and QAnon subverses in June 2015 and inSeptember 2018, respectively.
In this section, we set out to shed light on the narrativesof the QAnon movement on Voat aiming to answer RQ2.More specifically, we explore the most prominent topics that/v/GreatAwakening discusses, and detect the most popular en-tities they mention using entity detection. Finally, we useword embeddings and graph representations to visualize key-words most similar to the keyword “qanon.” We warn readersthat some of the content presented and discussed in this sec-tion may be disturbing.
We analyze the most prominent topics on /v/GreatAwakeningby running Latent Dirichlet Allocation (LDA) [12] on the textincluded in both the title and the body of all submissionsas well as their comments. For every post, we remove allthe URLs, stop words (e.g., “like,” “to,” “and”), and format-ting characters, e.g., \n, \r. Then, we tokenize each commentand create a term-frequency inverse-document frequency (TF-IDF) array, which is used to fit an LDA model. We use aTF-IDF array instead of the default LDA approach as TF-IDFstatistically measures the importance of every word within theoverall collection of words, and more importantly becauseprevious work suggests it yields more accurate topics [39].We also use guidelines from Li [66] to build the LDA model.In Table 2, we report the top ten topics, alongwith the words and their weights, discussed on both/v/GreatAwakening and the baseline subverses. For/v/GreatAwakening, users tend to discuss the US Presiden-tial Elections, as suggested by words like “trump,” “elect,”“biden,” and “vote” across many topics. There are also dis-cussions about the COVID-19 pandemic: “covid,” “mask,”“test,” and “vaccin” (topic 2). We also find a topic aboutthe “Black Lives Matter” movement, including hateful andrace-related words, such as “nigger,” “black,” “white,” (topic7 amed Entity ( % ) Entity Label % ) Trump (PERSON) 5,953 3.94 ORG 69,056 45.75one (CARDINAL) 3,623 2.40 PERSON 61,556 40.78first (ORDINAL) 2,670 1.76 GPE 31,286 20.74US (GPE) 2,022 1.34 DATE 29,496 19.54Biden (PERSON) 2,009 1.33 CARDINAL 26,155 17.32America (GPE) 1,733 1.14 NORP 20,665 13.69China (GPE) 1,660 1.09 WORK_OF_ART 5,481 3.63two (CARDINAL) 1,526 1.01 ORDINAL 5,225 3.46American (NORP) 1,505 0.99 TIME 4,126 2.73FBI (ORG) 1,447 0.95 LOC 3,900 2.58
Table 3:
Top 10 named entities and entity labels mentioned in/v/GreatAwakening.
While topic modeling gives us an idea of what is being dis-cussed, to get an understanding of who is being discussed, weextract the named entities used in our communities of inter-est. We do so in order to understand who conspiracies focuson and better define the narrative they might be pushing.To obtain the named entities mentioned in each post, weuse the en_core_web_lg ( v . ) model from the SpaCy li-brary [63]. We choose this specific model over alterna-tives, e.g., MonkeyLearn,since, to the best of our knowledge,it is trained on the largest training set. Moreover, previ-ous work [30] ranks this model as the second most accuratemethod for recognizing named entities in text. We select thissolution over the first one because [30] explain SpaCy detectsdates more accurately, compared to the one they rank first,Stanford NER. More specifically, SpaCy uses millions of on-line news outlet articles, blogs, and comments from varioussocial networks to detect and extract various entities from text.Crucially, for our purposes, the model also provides an entitycategory label in addition to the entity itself. For example, theentity category for “Donald Trump” is “person.” The differ-ent categories range from celebrities to nationalities, products,and even events. In Table 3, we list the ten most popular named entitiesand categories from /v/GreatAwakening. We note that apost may mention an entity more than once. Therefore, weonly report the number of posts that mention an entity atleast once. Unsurprisingly, considering his central role inthe QAnon conspiracy, “Donald Trump” is the most pop-ular named entity on /v/GreatAwakening with almost 6Kposts ( . ) mentioning him. Other popular entities in- See https://spacy.io/api/annotation
Named Entity ( % ) Entity Label % ) one (CARDINAL) 7,621 2.13 ORG 61,474 17.21jews (NORP) 6,385 1.78 PERSON 58,383 16.34first (ORDINAL) 5,401 1.51 NORP 40,808 11.42Jews (NORP) 4,804 1.34 GPE 37,947 10.62Trump (PERSON) 4,331 1.21 CARDINAL 35,050 9.81US (GPE) 3,571 0.99 DATE 34,657 9.70two (CARDINAL) 3,293 0.92 ORDINAL 9,043 2.53America (GPE) 3,142 0.88 LOC 8,060 2.25jewish (NORP) 2,948 0.82 WORK_OF_ART 7,320 2.05Jew (NORP) 2,305 0.64 PERCENT 5,816 1.62 Table 4:
Top 10 named entities and entity labels mentioned in all thebaseline subverses in our dataset. clude “US” ( . ), “Biden” ( . ), “America” ( . ),“China” ( . ), and “FBI”’ ( . ). The most popu-lar category is organizations ( . ), followed by people( . ). Other popular labels include nationalities, reli-gious, or political groups (NORP, . ), books, songs, andmovies (WORK_OF_ART, . ), and times ( . ).In comparison, in Table 4, we list the ten most popu-lar named entities and categories used in the baseline sub-verses. The most popular named entities for these subversesare “jews” ( . ), “Trump” ( . ), “America” ( . ),and “jewish” ( . ). The most popular labels organizations( . ), people ( . ), and nationalities, religious, orpolitical groups ( . ).Overall, this suggests that discussions within these com-munities are related to US happenings and events, politics,and established organizations and institutions. Baseline sub-verses focus mostly on nationalities, and religious or politi-cal groups, while /v/GreatAwakening discussions focus on theUS, President Trump, and the US Presidential elections. Word Embeddings.
To assess how different words are in-terconnected with popular QAnon specific keywords (e.g.,“qanon”), we use word2vec, a two-layer neural network thatgenerates word representations as embedded vectors [41]. Aword2vec model takes a large input corpus of text and mapseach word in the corpus to a generated multidimensional vec-tor space, yielding a word embedding . Words that are used insimilar contexts tend to have similar vectors in the generatedvector space.To clean the QAnon posts before training the model, wefollow a similar methodology as for the topic modeling pre-sented Section 5.1. We train the word2vec model using a con-text window (which defines the maximum distance betweenthe current word and predicted words when generating theembedding) of 7, as suggested by [35]. We limit the cor-pus to words that appear at least 50 times, due to the smallsize of our dataset. Finally, we train the word2vec model with8 iterations (epochs) as, on small corpuses like ours, epochsbetween 5 and 15 epochs are suggested to provide the bestresults [41, 42]. (Choosing more epochs than 8 makes ourmodel overfit and minimizes the word vocabulary, e.g., re-moving QAnon-specific keywords like “qanon.”) After train-8 anon conspiracy theoriesaj followers q theory discredit qanonsconspiraciespsyoplarp movement tweets proofs disinformationdebunked labelmisinformationassumptiondisinfo bogus claims suggests iialeaks questionableconclusioninfowarsalex intel questioningpizzagate decodealt crumbscorsi anons credibilitysnowdenchans mentioningjones posts cultshilldrops marker confusedistract narrativesinsertcensor revealing manipulatedpremiseexposing manipulate truthsnotionacknowledgedeceivedefineaccuseprojectiondeceptionawaken believersreject normies embrace purpose denialreveals fiction mythregard science scientific facts promotescollusion cabal believing ds predicted convinceddoubts anon groups fascismideologyunitydivide divided tweet trending kunlatest tweeted twitter qmapreplies commented comments deleted pub posted instagram updatedheadline referenceestcommentingparlertwatter rumorsthreadsupdate octreferenceddatesdelete thread referringtiktok links recent topics interviewsdiscussed mentioned mentions references articleschan sundance coincidences legitcryptic timing relevanttimeline hopefulcomms narrative silencecoordinatedmanipulation Figure 6:
Graph representation of the words associated with the term “qanon” on Voat. We extract the graph by finding the most similarwords, and then we take the 2-hop ego network around “qanon.” In this graph the size of a node is proportional to its degree; the color of anode is based on the community it is a member of; and the entire graph is visualized using a layout algorithm that takes edge weights intoaccount (i.e., nodes with similar words will be closer in the visualization). “qanon” “q”Word Cos Similarity Word Cos Similarity conspiracy 0.636 anons 0.679theories 0.582 larp 0.594q 0.579 qanon 0.579movement 0.570 drops 0.570followers 0.561 proofs 0.557conspiracies 0.549 cryptic 0.545tweets 0.547 psyop 0.531aj 0.545 posts 0.529qanons 0.544 anon 0.526discredit 0.538 crumbs 0.524
Table 5:
Top ten similar words to the term “qanon” and “q” and theirrespective cosine similarity. ing, our model includes a 5.6K word vocabulary.
QAnon similar keywords.
Next, we find the top ten mostsimilar words to “qanon” and “q” according to the model; seeTable 5. We see that “qanon” is linked to words like “con-spiracy,” “theories,” “movement,” and “pizzagate.” The term“q” seems to be closely related to the activity of Q himself,and the research the community does to decode his crypticmessages. This is evident due to “drops,” which refer to thespecific, cryptic posts that Q leaves as breadcrumbs of infor-mation for adherents of the conspiracy to decode. These dropsoften hint at “psyops”, the alleged psychological operationsthe deep state and governments deploy to control society. In-terestingly, the term “larp,” an acronym for “Live Action RolePlaying,” is sometimes used in a derogatory fashion to implythat Q is just a troll playing a game. This indicates that evenon a community devoted to the QAnon conspiracy, there is atleast some degree of push back or dissent within the user base.We use graph representations to analyze this finding below.
Graph representations.
Finally, we follow the methodologyby Zannettou et al. [71] to visualize topics within the wordembeddings. Specifically, we transform the embeddings intoa graph, where nodes are words and edges are weighted by the cosine similarity between the learned vectors of the nodes theedge connects. We perform community detection [13] on theresulting graph, to gain new insights into the high-level topicsthat groups of words form.
Visualization.
Figure 6 shows the two-hop ego network [5]centered around the word “qanon.” Whereas, Figure 7 de-picts a graph centered around “q.” To improve readability(since our graph transformation results in a fully connectednetwork), we remove all edges with a cosine similarity lessthan 0.6. We further color each node based on the commu-nity it belongs to. Finally, we apply the ForceAtlas2 algo-rithm [29], which considers the weight of the edges when lay-ing out the nodes in the 2-dimensional space before producingthe final visualization.
Remarks.
Taking into account how communities form dis-tinct themes, and that nodes’ proximity implies contextualsimilarity, we observe from Figure 6 that the “qanon” com-munity (blue) is very close to the purple community, whichseems to be discussing the movement itself (“qanons,” “be-lievers”), while the blue community is discussing details ofthe conspiracy theory itself (“cabal,” “psyop,” “manipula-tion”). Next, the yellow community is focused on Q drops(“drops,” “timeline,” “decode”). In the green community, wecome across the QAnon predecessor “pizzagate,” Q drop ag-gregators (e.g., “qmap,” which was recently shut down [68]),and other social media platforms (8“kun”, “twitter,” “insta-gram,” and 4“chan”).Focusing on the contriver of the conspiracy theory, Figure 7plots the discussion around Q. Interestingly, the communityof “q” (red) has words like “larp,” “disinfo,” “doubts,” and“shill” (a term used for someone that might be hired by thegovernment pretending to agree with a conspiracy) in closeproximity of Q. On the other hand, we find terms like “fol-lowers” and “aj” (a term used to describe a man as support-ive and perfect). This plot strengthens the hypothesis that al-though the community is devoted to the QAnon movement, at9 anons proofs qanon larp drops crumbs anon posts chanscrypticdecodethreads chan kundisinfo calmconclusions qmap commsmemes normies aj markerfollowerspredicted sundance coincidenceslegit timing tweetsrelevantdoubts timeline pubhopeful questioning conspiracy theories theory discreditqanonsconspiraciespsyop movementshill drop topicsdates pol
Figure 7:
Graph representation of the words associated with the term“q” on Voat. the same time, there might be signs of chasm with regards towhat the users on /v/GreatAwakening think of Q.
The analysis presented in this section allows us to identifyand visualize the narrative around QAnon discussion (RQ2).We show that the QAnon community discusses online socialmedia, political matters, and world events. Additionally, themain topic of conversation is President Donald Trump, andthe US overall, and entities discussed are most typically or-ganizations and individuals. These findings confirm that, re-gardless of the particular components of the conspiracy the-ory, Trump’s role in the conspiracy, e.g., as the alleged leaderin the war against the deep state, is central.Finally, our structural analysis of word embedding similar-ities provides some high level topics of discussion within thecommunity. For example, we find that the term “larp,” an oftused criticism of Q implying he is merely playing a game, isoften used in the same context as discussion of “qanon” him-self. This is an indicator that adherents are well aware of crit-icisms of the source of their information, and perhaps somedissent within the community itself. Additionally, we see thatthe movement is well embedded across the Web, with externalq-drop aggregators (e.g., qmap) and social media platformsare commonly discussed along with Q.
In this section, we analyze the toxicity of the/v/GreatAwakening community, compared to the gen-eral discussion subverses. Motivated by our findings inSection 5.1, which suggest toxicity, hate, and racism to be existing in all subverses of our dataset, we analyze thecontent of each post according to how toxic, obscene, insult-ing, profane, and inflammatory they are. To do so, we useGoogle’s Perspective API [48]. We choose this tool, similarto prior work [47], as other methods mostly use short texts(tweets) for training [21], whereas, Google’s Perspective APIis trained on crowdsourced annotations and comments withno restriction in character length, similar to Voat posts.We rely on six models to annotate posts from all subverses:• toxicity : how rude or disrespectful a post is;• severe_toxicity : same as toxicity but less sensitive toposts that include positive uses of curse words.• obscene : provides high scores for messages that likelycontain indecent language.• insult : quantifies how likely a message is to be negativeand insulting towards an individual or a group.• profanity : calculates the likelihood of a message contain-ing slurs, swear, or curse words.• inflammatory : how likely it is for a message to irritateothers towards “inflaming” the discussion.Note that all methods provide scores for textual posts. There-fore, we do not have scores for . (24.6K) of the posts inour dataset, since they only contain links or images, but notext.In Figure 8, we plot the CDF of the scores for eachmodel. The baseline subverses (B in Figure 8(a)) exhibithigher levels of toxicity and severe_toxicity , compared to/v/GreatAwakening (Q in the figure). Specifically, . and . of the baseline posts have, respectively, toxicity and severe_toxicity scores greater than 0.5, while only . and . of the QAnon posts have these scores greater than 0.5.We observe similar trends for the other models with the base-line subverses scoring always higher than /v/GreatAwakening.Overall, . and of the baseline subverses’ posts havean obscene and insult score greater than 0.5, respectively (Fig-ure 8(b)), and . for profanity and for inflammatory (Figure 8(c)). For all six models, the percentage of the QAnonposts that have perspective score greater than 0.5, was at least smaller than the general discussion posts. Last, we usetwo-sample KS test to check for statistically significant dif-ferences between all the distributions in Figure 8 and find thep-value on each pair ( p < . ). Remarks.
Although the content of the QAnon communityexhibits some levels of toxicity, the movement is not as toxicas other discussions on the platform. We believe this not tobe entirely surprising as the community seems to be more fo-cused on the conspiracy aspects of world events, politics, andPresident Trump, while racist and/or hateful agendas mightmore vigorously characterize Voat as a whole, or at least thepopular general-discussion subverses in our baseline. In otherwords, toxicity in the discussions seem to be targeted to-wards the so called “deep-state,” the puppet masters, and thepedophile ring members. Whereas, baseline subverses like/v/news and /v/politics are likely to include inflammatory dis-cussions between users with contradicting opinions or com-ment on world events from a racist/hateful standpoints.Interestingly, the level of toxicity in the baseline subverses10 .0 0.2 0.4 0.6 0.8 1.0Perspective Score0.00.20.40.60.81.0 C D F TOXICITY (Q)SEVERE_TOXICITY (Q)TOXICITY (B)SEVERE_TOXICITY (B) (a) C D F OBSCENE (Q)INSULT (Q)OBSCENE (B)INSULT (B) (b) C D F PROFANITY (Q)INFLAMMATORY (Q)PROFANITY (B)INFLAMMATORY (B) (c)
Figure 8:
CDF of the Perspective Scores related to how toxic, severely toxic, obscene, insulting, profane, or inflammatory a post is for the/v/GreatAwakening (Q) and baseline (B) subverses. appear to be similar to that of 4chan’s /pol/, as presentedin [47]. In particular, we find that the percentage of poststhat get scores above 0.5, across all models, are very simi-lar on /pol/ and our four baseline subverses. Considering that/pol/ is broadly considered to be a highly toxic place [27], thissuggests that Voat is too.
In this section, we review previous work on QAnon and Voat.
Qualitative work around QAnon.
Prooijen [28] studies whypeople tend to believe in conspiracy theories like QAnon, ar-guing that their beliefs are not necessarily pathological ornovel, and can be followed by individuals who behave rela-tively normally. The author explains that, typically, individu-als follow more than one conspiracy theory, as also discussedby Goertzel [25], and that they believe that nothing happenscoincidentally. At their core, conspiracy theories reinforcethe idea that hostile or secret machinations permeate all so-cial layers, thus forging an appealing account of events forthe individuals that seek “explanations.” Finally, followersare likely to experience anxiety and uncertainty, which oftenlead them to try to understand societal events that traumatizedthem.Sternisko et al. [64] argue that conspiracy theories, includ-ing QAnon, pose a real threat to democracies, as governmentofficials and media might start or amplify them to benefit theirpolitical agendas and interests. Schabes [61] stresses that so-cial networks help conspiracy theories spread faster, whichin turn threatens individual autonomy and public safety, en-forces political polarization, and harms trust in governmentand media. Rutschman [59] studies misinformation spreadby the QAnon movement on the Web, e.g., claiming thatBill Gates orchestrated the COVID-19 outbreak and claim-ing that drinking “Miracle Mineral Supplement,” commonlyknown as chlorine dioxide, prevents infections. Thomas andZhang [67] explain that small groups of engaged conspir-acists, like QAnon followers, can potentially influence rec-ommendation algorithms to expose new, unsuspecting usersto their beliefs. The same study notes that conspiracy theoriesoften include information from legitimate sources or officialdocuments framed with misleading and conspiratorial expla- nations to events. This creates an illusion of an explanationand further complicates moderation efforts against conspira-torial content.
Quantitative work on QAnon.
McQuillan et al. [38] collect81M tweets related to COVID-19 between January and May2020, finding that the QAnon movement not only has grownthroughout the pandemic, but also that its content has reachedmore mainstream groups. In fact, the Twitter QAnon commu-nity almost doubled in size within two months. Darwish [20]gather 23M tweets related to US Supreme Court judge BrettKavanaugh for 3 days and 4 days in September and October2018, respectively. They find that the hashtags precision. UsingTF-IDF, they also find that, within the top 15 discriminat-ing words in the snippet of the videos of the training set, theterm “qanon” ranks third. Also, QAnon-related videos belongto one out of the three top topics identified by an unsuper-vised topic modeling algorithm. The authors conclude thatYouTube’s recommendation engine might operate as a “filterbubble.”
Voat.
Chandrasekharan et al. [17] detect abusive contentusing data from 4chan, Reddit, MetaFilter, and Voat, andrelying on a novel approach called Bag of Communities(BoC). Part of the Voat data collected for their work originatefrom /v/CoonTown, /v/Nigger, and /v/fatpeoplehate: threecommunities focused on hate towards groups of individu-als with specific body or race characteristics. These sub-verses were created in Voat after Reddit banned the origi-nal /r/CoonTown, /r/fatpeoplehate, and /r/nigger subreddits in11015 [57, 55, 52]. Similarly, Salim et al. [58] use Reddit com-ments to train a classifier to detect hateful speech includingon Voat’s /v/CoonTown, /v/fatpeoplehate, and /v/TheRedPill.Khalid and Srinivasan [34] collect 872K comments from/v/politics, /v/television, and /v/travel in an attempt to detectdistinguishable linguistic style across various communities;more specifically, they compare the features of Voat com-ments to Reddit and 4chan comments and train a classifier topredict the origin of the comments based on its style and con-tent. Finally, Popova [49] uses data from Voat’s /v/DeepFakeand the site mrdeepfakes.com, finding pornographic deep-fakes created for circulation and enjoyment within the com-munity. Note that both the mrdeepfakes.com and the subverse/v/DeepFake were created after Reddit banned the subreddit/r/DeepFakes in 2018 [53, 26].
Remarks.
Previous quantitative work related to QAnon hasmostly focused on Twitter [18, 38], while ours does so onVoat. Overall, our paper presents, to the best of our knowl-edge, the first characterization of the QAnon community onVoat. Some of our findings are, to some extent, aligned withthose from previous studies; in particular, we too observea steady increase in posting activity on /v/GreatAwakening,somewhat similar to [38], which finds that the QAnon move-ment on Twitter increased in size over their collection period.Overall, this study is the first to collect and characterize theQAnon movement on Voat.
This work presented a first characterization of the QAnonmovement on the social media aggregator site Voat.We collected over 510K posts from five subverses:/v/GreatAwakening, the largest QAnon-related subverse, aswell as a baseline consisting of the four most active subverses,/v/news, /v/politics, /v/funny, and /v/AskVoat.We showed that users on both the QAnon and base-line subverses tend to be engaged, but the audience of/v/GreatAwakening (20K subscribers) consumes data fromjust a handful of content creators who are responsible for over . of the total submissions in the community. In addi-tion, we found that /v/GreatAwakening users had a peak inregistration activity shortly after Reddit banned QAnon re-lated communities in September 2018. Using topic model-ing techniques, we showed that conversations focus on worldevents, US politics, and President Trump. We also trained aword2vec model to illustrate the connection of different termsto closely related words, finding that the terms “qanon” and“q” are closely related to other conspiracy theories like Piz-zagate, other social networking platforms, the so-called deep-state, and “research” activities the community performs to de-code Q’s cryptic posts. Finally, toxicity scores from Google’sPerspective API shows that posts in /v/GreatAwakening are less toxic than those on popular general-discussion (baseline)subverses.Although this paper represents the first large-scale study ofthe QAnon movement on social media, it is far from com- prehensive, and numerous questions about the movement re-main, leaving several directions for future work. First, whilethis paper focused on Voat, the QAnon movement is decid-edly multi-platform, and thus we encourage work that exam-ines it from a cross-platform perspective. Next, even thoughit has only recently entered mainstream discourse, QAnon hasa long and still somewhat muddied evolution. This calls forlongitudinal studies that cover a much longer period than thatin the present work to get a firm grasp on how the movementhas evolved, both in terms of components of the conspiracyas well as user engagement and discussion (e.g., how do ad-herents react when the predictions in a q-drop do not cometo pass). Finally, we believe that while understanding themovement itself is important, there are real indications thatit exhibits cult-like characteristics, e.g., recovery stories fromformer adherents [50, 36] and communities devoted to emo-tional support for people whose loved ones have become ad-herents [1], it is crucial to understand more about the QAnon counter-movement which might provide insights into the real-world impact of the spread of this and other dangerous con-spiracy theories as well as devising mitigation strategies. Acknowledgments.
This project was partly funded by theEPSRC grant EP/S022503/1, which supports the Center forDoctoral Training in Cybersecurity delivered by UCL’s De-partments of Computer Science, Security and Crime Science,and Science, Technology, Engineering and Public Policy.
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