Conspiracy theories in the digital world – How does the digital language surrounding QAnon conspiracy theories differ on platforms such as Twitter and Reddit through the lens of “Face” and “Mask” culture?

By: Lara R.
On: October 21, 2020
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About Lara R.


Authors: Christian R. , Doortje N., Lara., Sara N.

Conspiracy theories are in no way something new and have, in fact, already been circulating and believed for a long time, some of the most famous examples being the conspiracy theory about the US having faked the moon landing in 1969 or that 9/11 was an inside job by the American government. Conspiracy theories are defined as follows: “a proposed plot by powerful people or organizations working together in secret to accomplish some (usually sinister) goal.” (Wood, Michael J., et al.).
With the help of social media and specifically anonymous message boards, such as 4chan or Reddit, it has become easier for people to spread their conspiracy theories and easier for people that enjoy them to find them, as well as ‘fall into the rabbit hole’ of receiving more and more theories as recommended content. With the rise in popularity of QAnon content, we propose the following research question: How does the digital language surrounding QAnon conspiracy theories differ on platforms such as Twitter and Reddit through the lens of “Face” and “Mask” culture?

What is QAnon and how did they emerge?

In October 2017, someone who named themselves “Q” posted on the anonymous platform “4chan”, claiming they have high US government clearance and that they “knew the truth about the secret struggle for power between Trump and the ‘deep state’”, in which “Hillary Clinton’s ‘extradition’ was ‘already in motion’” (Wong). QAnon essentially believes that a “cabal” of politicians, celebrities and billionaires (including Hillary Clinton and Barack Obama) are human traffickers and paedophiles and use the blood of abused children to extend their life. They believe that Trump will soon succeed in locking all of them up in Guantanamo Bay (Relman). The term “cabal” comes from the 20th-century antisemitic conspiracy theory of the “Protocols of the Learned Elders of Zion”, which claimed to be ‘proof’ that Jewish people are planning to take over the world (Dunlap). This theory has been debunked in 1921 and proofed to be entirely made up of lies to justify discrimination.
During the US presidential elections in 2016, QAnon was also part of the “Pizzagate theory”, which claimed to have the aforementioned child-trafficking ring in a Washington DC pizza restaurant. One person believed this theory so strongly that he opened fire in the restaurant, thinking that children needed to be saved (Griffin).

Because of the positive interpretation of Trump’s presidency and his ‘war’ against the ‘deep state’, in their circles also referred to as “the storm”, is the main focus of the conspiracy theories, most QAnon followers appear to be republican, right-wing Trump supporters (Wong). Even though none of their theories has turned out to be true (e.g. Clinton’s apparent imminent arrest), QAnon supporters tend to find new theories and reasons for that. One recent example is Trump having contracted COVID-19. Many QAnon followers believe that this is a hoax and he only went to the hospital to be protected from the ‘war’ against the ‘deep state’ enemies, others believe that COVID-19 itself is a hoax, others believe that the virus was created to hurt Trump and stop him from ‘success’ (Collins and Zadrozny).

The beliefs have gained more attraction on anonymous message boards and mainstream social media and have taken over the hashtag #SaveTheChildren to spread their beliefs about the child trafficking theories in particular. Due to these innocent-looking hashtags, some outsiders and non-politically involved people are also more likely to support them and ‘fall into the rabbit hole’ (North). In an attempt to stop the spread of misinformation, Facebook and Twitter banned QAnon groups in the summer of 2020, with TikTok following suit in October 2020 (Lyons). It is uncertain how many followers QAnon exactly has, but based on members in these social media groups and real-life demonstrations in several countries, it is safe to estimate that there are several hundreds thousands of followers in the US, Europe and South America.

Image 1: QAnon supporters at a Trump rally in 2020

Affordances of different media platforms

Affordances, according to Evans et al., mediate the variable process between the properties of an artefact and what subjects do with the properties of an object. Therefore, it is the multifaceted relational structure between an object and the use that enables or constrains potential behavioural outcomes in a particular context (Davis and Chouinard). In simplified words, affordances include an array of potential uses of an object or artefact, which could go from practical physical uses to more abstract requests, demands of encouragements of an object (Davis and Chouinard). When linked to social media platforms, affordances can work both from the platform to the user and also the other way around. For example, Twitter’s like- button enables the user to communicate with other users, advertisers, but also with the platform. As the algorithmic timeline of Twitter affords users to contribute to the relevance of tweets by sharing, retweeting and replying to tweets, instead of only functioning on the receiving end (Bucher and Helmond).

To examine the differences in affordances of both platforms, it is useful to look at the division between “mask culture” and “face culture” on social media platforms. The division between these two cultures is determined by the extent to which the users of the platforms are represented on the platform as themselves. Platforms like Reddit and 4Chan afford their users very different interactions with the platform and other users, in comparison to platforms like Facebook. The former two platforms could tend to fit more to the “mask culture”, as hardly any users have usernames close to their first and last name, no true profile pictures and the “deep vernacular” or jargon that is used on these platforms is non-comprehendible for non-users (De Zeeuw and Tuters). These platforms, therefore, facilitate more exclusionary use in some sense, because they tend to present “informal spaces of leisure and play outside of the spheres of work and school.”  (De Zeeuw and Tuters). Whereas platforms like Facebook are characterized by “face culture”, except for some bots and trolls, the majority of users are truthful to their physical representatives. Because of the findability of persons in the real world, there is a shift in accountability too. The authenticated, identity-driven model, in combination with the more regular vernacular in these “face cultures”, afford for entirely different uses in comparison to Reddit or 4Chan (De Zeeuw and Tuters). Facebook is more easily recorded and analyzed for corporate profit because it links off- and online activities and identities as part of a single personal profile and corresponding data double (De Zeeuw and Tuters). Twitter, as a social media platform, might be more difficult to position in this spectrum as it seems to combine characteristics to both “mask-” and “face culture” up until a certain degree.

The presence of misinformation or ‘fake news’ has increased in the current comprehensive digital realm, resulting in it’s growing role in polarization and influence in worldwide politics. Looking at the 2016 United States presidential elections, it has been clear that the average American adult was exposed to multiple ‘fake news’ articles regarding the elections, of which more were pro-Trump than pro-Clinton. Furthermore, results show that a ‘fake news’ article can be just as persuasive as one TV Campagne (Allcott and Gentzkow). This research identifies the motivation for producing ‘fake news’ to be either pecuniary or ideological, the latter seems to apply to QAnon. When looking at the consumptions of ‘fake news’, research suggests various factors are influencing who believes them, depending on both on demographic and the content (Allcott and Gentzkow). We used this ‘fake news’ theory concerning politics because it is very relevant to our research, yet the researchers also highlight their numbers are estimates because it is inevitable that some content will be missed. Moreover, one ‘fake news’ article might not affect as much as another, so there is a component of cheering for a party due to personal preference. For example, Republicans are more likely than Democrats to believe that President Obama was born outside the United States (commonly known as the “birther movement” and taking this theory so far as to ask “Where’s the birth certificate?” on billboards in the US, see image 2 below), and Democrats are more likely than Republicans to believe that President Bush was complicit in the 9/11 attacks (Cassino and Jenkins). Identifying ‘fake news’ sites and articles also raise important questions about who becomes the arbiter of truth (Allcott and Gentzkow).

Image 2: A US billboard questioning the authenticity of Obama’s birth certificate in the context of the “birther movement” 

Table 1: Result of the sentiment analysis


We performed a sentiment analysis on 1250 tweets and the top 5 comments for 250 Reddit posts. To process the text and perform the analysis we used TextBlob, a Python library for processing textual data. Using Textblob, we calculated both the polarity and the subjectivity in the text. For the polarity score, Textblob returns a number between -1 and 1, where -1 means the text is negative and 1 means it is positive. A subjectivity score is a number between 0 and 1, with 0 meaning the text is very objective and 1 meaning it is subjective (Loria). 

In image 3 and 4, two separate word clouds are shown. The clouds are made using several libraries in Python. The code collects all the text, in the case of Twitter the 1250 tweets and for Reddit the 1250 comments, and counts the most occurring words. These words are visualized in the word clouds, with the size of the words representing the rank of their occurrence. 

Image 3: Word Cloud from tweets containing #QAnon

Image 4: Word cloud from Reddit posts containing #QAnon 

The word clouds illustrate that the majority of the tweets feature words related to other conspiracies, such as “conspiracy”, “jfk” or “paedophilia”, while the Reddit posts mainly include words such as “politics”, “voting”, action”. These results were surprising as Reddit is more commonly known to contain a lot of conspiracy theories and Twitter usually contains more politically-motivated posts, based on our own experience and theoretical framework. 

We used both a quantitative and qualitative approach by combining a theoretical affordance analysis paired with a data sentiment analysis. Thus, our research question may be defined as: How does the digital language surrounding QAnon conspiracy theories differ on platforms such as Twitter and Reddit through the lens of “Face” and “Mask” culture?  This approach was helpful because QAnon is a digitally native movement that began on anonymous imageboards and message boards before spreading to other social media sites like Twitter, Facebook and Instagram.

However, it is important to consider the limits of data analysis; as demonstrated by various texts, numbers are not always an accurate representation of content. In “Critical Questions For Big Data,” the authors discuss how data’s claims to objectivity can be misleading (Boyd and Crawford 666). They elaborate on the importance of interpretation when using data analysis to counter biases and limitations as they write: “Data analysis is most effective when researchers take account of the complex methodological processes that underlie the analysis of that data.” (Boyd and Crawford 668).

A similar criticism can be applied to data visualization. According to Lev Manovich, visualization relies on two key principles: reduction and spatial variables. (Manovich 36) As a result, data visualization can lead the researcher to “throw away 99 per cent of what is specific about each object to represent only 1 per cent – in the hope of revealing patterns across this 1 per cent of objects’ characteristics.” (Manovich 38).

 We took these criticisms into account and, for that reason, it was helpful to apply a theoretical affordance framework to our methodology. To ensure a well-rounded understanding, we brought together multiple research methods including data visualization, data sentiment analysis, and critical theoretical analysis.

We were able to use the aforementioned theoretical framework to interpret our data results to understand patterns surrounding fringe internet communities, and the difference in communication style on the two platforms. We found that users were more subjective in their posts relating to QAnon on Reddit. We can interpret this result by understanding the lack of censorship, verification, and individual identity on anonymous centric platforms like Reddit that may lead users to speak more openly about taboo topics such as conspiracy theories. We can also understand how Reddit’s platform affords users to use subreddits, or subcategories, which encourages more niche discussions perhaps creates a stronger sense of community around particular topics.


As far-fetched as these theories seem at first glance, QAnon’s followers can, unfortunately, not be ignored. As aforementioned, QAnon is gaining more and more followers worldwide and is supported by demonstrations in several countries. One of the reasons for that are hashtags such as #savethechildren, which draw in groups of people that were not interested in conspiracy theories or even politics beforehand, yet get invested in the theories because they believe that it is for a good cause and ‘fall down the rabbit hole’. 

QAnon’s Twitter and Facebook groups had more than a hundred thousand members before they were removed by the platforms. Several platforms are now actively removing QAnon content to stop the spread of misinformation. Moreover, several candidates that are running for the US congress have openly supported QAnon (Mahdawi), which could potentially harm US citizens and it also does not help that Trump himself does not explicitly debunk the theories about his waging ‘war’ against the ‘deep state’. 

As discussed before, the difference between “mask” and “face” digital culture boils down to how users engage with content, either anonymously or as a digital data double of their real-life “IRL” self. Further, “mask” cultures provide a space outside of work for end-users to feel liberated in how they want to conduct themselves, where they are less pressured to follow societal norms. As a result, these anonymous messageboards may be more conducive to discussing and spreading subversive topics (like QAnon theories) that may not be more heavily censored (or simply deemed socially unacceptable to discuss) on other platforms.

As indicated, both the qualitative and quantitative aspects of this research have limitations due to the datasets and the subjective nature of the arbitering of fake news. Nevertheless, the theoretical lens offered by De Zeeuw and Tuters has helped to examine the diverse platform affordances of Reddit and Twitter. As expected, the “mask culture” thriving on the anonymous platform Reddit does result in more subjectivity. The results also show that Twitter’s relative “face culture” does not imply the exact opposite, yet the numbers are lower. The more categorized subreddits might induce subjectivity in smaller congenial communities, versus the more open and judgemental structure on Twitter. The word clouds also show a difference between Twitter and Reddit’s most occurring words. Where the Twitter world cloud shows more words related to conspiracy theories, Reddit’s word cloud presents more words connected to the US presidential election. However, it is to note that a lot of words were very similar, so there are only slight distinctions and no concrete link to the words being more related to “mask culture” or “face culture”. Due to the limitations of this research regarding the effect of the messages and the fact that Twitter is more of a “semi-face culture” than a full “face culture” like Facebook, it is difficult to pin down the exact differences in which users talk about QAnon. This suggests that the role of “regular” social media should not be underestimated in regards to spreading conspiracy theories about QAnon. With their unique tactics and use of hashtags, they manage to ‘recruit’ people that use mainstream and also anonymous media platforms. 


 Allcott, Hunt, and Matthew Gentzkow. ‘Social Media and Fake News in the 2016 Election’. Journal of Economic Perspectives, vol. 31, no. 2, 2017, pp. 211–36.

 Boyd, Danah, and Kate Crawford. “Critical Questions for Big Data.” Information, Communication & Society, vol. 15, no. 5, Routledge, June 2012, pp. 662–79. Taylor and Francis+NEJM, doi:10.1080/1369118X.2012.678878.

 Bucher, Taina, and Anne Helmond. ‘The Affordances of Social Media Platforms’. The SAGE Handbook of Social Media, Sage London and New York, NY, 2017, pp. 233–253.

  Collins, Ben, and Brandy Zadrozny. QAnon Followers Elated at Trump Covid News. 10 Mar. 2020,

 Davis, Jenny L., and James B. Chouinard. ‘Theorizing Affordances: From Request to Refuse’. Bulletin of Science, Technology & Society, vol. 36, no. 4, SAGE Publications Sage CA: Los Angeles, CA, 2016, pp. 241–248.

 De Zeeuw, Daniël, and Marc Tuters. ‘Teh Internet Is Serious Business: On the Deep Vernacular Web and Its Discontents’. Public Culture, vol. 16, no. 2, Duke University Press, 2020, pp. 214–232.

  Dunlap, David W. ‘1920-21 | Exposing the “Protocols” as a Fraud (Published 2016)’. The New York Times, 27 Oct. 2016,

  Griffin, Andrew. ‘The Bizarre Hillary Clinton Conspiracy Theory That Led a Man to Open Fire in an Italian Restaurant’. The Independent, 5 Dec. 2016,

 Loria, Steven. TextBlob: Simplified Text Processing. 2020, 

  Lyons, Kim. ‘TikTok Removing Accounts of Users Who Share QAnon-Related Content’. The Verge, 19 Oct. 2020,

  Mahdawi, Arwa. ‘The Most Unhinged Trump Conspiracy Theory Comes from – Who Else? – QAnon Followers | Arwa Mahdawi’. The Guardian, 3 Oct. 2020,

 Manovich, Lev. “What Is Visualisation?” Visual Studies, vol. 26, no. 1, Routledge, Mar. 2011, pp. 36–49. Taylor and Francis+NEJM, doi:10.1080/1472586X.2011.548488.

  North, Anna. ‘How #SaveTheChildren Is Pulling American Moms into QAnon’. Vox, 18 Sept. 2020,

 Relman, Eliza. ‘The pro-Trump conspiracy theory known as “QAnon” has moved from the fringes of the internet to feature prominently at Trump rallies’. Business Insider Nederland, 29 Mar. 2019,

  Wong, Julia Carrie. ‘QAnon Explained: The Antisemitic Conspiracy Theory Gaining Traction around the World’. The Guardian, 25 Aug. 2020,

 Wood, Michael J., et al. ‘Dead and Alive: Beliefs in Contradictory Conspiracy Theories’. Social Psychological and Personality Science, vol. 3, no. 6, Nov. 2012, pp. 767–73, doi:10.1177/1948550611434786.

References for images:

Image 1: ‘Free Online Word Cloud Generator and Tag Cloud Creator’. Wordclouds.Com, Accessed 19 Oct. 2020.

Image 2:  Victor, Victoria. ‘Barack Obama Citizenship Conspiracy Theories’. Wikipedia, 19 Oct. 2020,

Image 3, 4: Smit, Luuk. ‘QAnon resoluut afgewezen door Amerikaans Huis van Afgevaardigden · CIDI’. CIDI, 5 Oct. 2020,

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