Comparing the Language of QAnon-related content on Parler, Gab, and
Twitter
- URL: http://arxiv.org/abs/2111.11118v1
- Date: Mon, 22 Nov 2021 11:19:15 GMT
- Title: Comparing the Language of QAnon-related content on Parler, Gab, and
Twitter
- Authors: Andrea Sipka, Aniko Hannak, Aleksandra Urman
- Abstract summary: Parler, a "free speech" platform popular with conservatives, was taken offline in January 2021 due to the lack of moderation of hateful and QAnon- and other conspiracy-related content.
We compare posts with the hashtag #QAnon on Parler over a month-long period with posts on Twitter and Gab.
Gab has the highest proportion of #QAnon posts with hate terms, and Parler and Twitter are similar in this respect.
On all three platforms, posts mentioning female political figures, Democrats, or Donald Trump have more anti-social language than posts mentioning male politicians, Republicans, or
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Parler, a "free speech" platform popular with conservatives, was taken
offline in January 2021 due to the lack of moderation of hateful and QAnon- and
other conspiracy-related content that allegedly made it instrumental in the
organisation of the storming of the US Capitol on January 6. However, Parler
co-existed with other social media platforms, and comparative studies are
needed to draw conclusions about the prevalence of anti-social language, hate
speech, or conspiracy theory content on the platform. We address this through a
cross-platform comparison of posts related to QAnon. We compare posts with the
hashtag #QAnon on Parler over a month-long period with posts on Twitter and
Gab. In our analysis, Parler emerges as the platform with the highest average
toxicity of the posts, though this largely stems from the distinctive way
hashtags are used on this platform. Gab has the highest proportion of #QAnon
posts with hate terms, and Parler and Twitter are similar in this respect. On
all three platforms, posts mentioning female political figures, Democrats, or
Donald Trump have more anti-social language than posts mentioning male
politicians, Republicans, or Joe Biden. An analysis of entities mentioned in
posts revealed differences in content - with Twitter posts mentioning prominent
figures related to QAnon, while Parler and Gab posts mention entities related
to conspiracies. Narrative analysis indicates that the discussion on Twitter
centres on QAnon and is critical of it, Parler focuses on supporting Donald
Trump, while on Gab the discussion focuses on more conspiratorial content, in
relation to both Trump and other political figures.
Related papers
- Predicting Hate Intensity of Twitter Conversation Threads [26.190359413890537]
We propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future.
It uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet.
We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly.
arXiv Detail & Related papers (2022-06-16T18:51:36Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - Rise of QAnon: A Mental Model of Good and Evil Stews in an Echochamber [0.0]
The QAnon conspiracy posits that Satan-worshiping Democrats operate a covert child sex-trafficking operation.
We report two computational studies examining the social network structure and semantic content of tweets produced by users central to the early QAnon network on Twitter.
arXiv Detail & Related papers (2021-05-10T19:34:35Z) - The Gospel According to Q: Understanding the QAnon Conspiracy from the
Perspective of Canonical Information [10.788583114755838]
We study the QAnon conspiracy theory from the perspective of "Q" themself.
We build a dataset of 4,949 canonical Q drops collected from six "aggregation sites"
We analyze the Q drops' contents to identify topics of discussion and find statistically significant indications that drops were not authored by a single individual.
arXiv Detail & Related papers (2021-01-21T18:03:24Z) - Capitol (Pat)riots: A comparative study of Twitter and Parler [37.277566049536]
On 6 January 2021, a mob of right-wing conservatives stormed the USA Capitol Hill interrupting the session of congress certifying 2020 Presidential election results.
Immediately after the start of the event, posts related to the riots started to trend on social media.
Our report presents a contrast between the trending content on Parler and Twitter around the time of riots.
arXiv Detail & Related papers (2021-01-18T07:46:14Z) - "Is it a Qoincidence?": An Exploratory Study of QAnon on Voat [12.14455026524814]
The QAnon conspiracy theory emerged in 2017 on 4chan.
We study the most popular named entities mentioned in the posts, along with the most prominent topics of discussion.
Our graph visualization shows that some of the QAnon-related ones are closely related to those from the Pizzagate conspiracy theory.
arXiv Detail & Related papers (2020-09-10T14:25:28Z) - Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media
during the COVID-19 Crisis [51.39895377836919]
COVID-19 has sparked racism and hate on social media targeted towards Asian communities.
We study the evolution and spread of anti-Asian hate speech through the lens of Twitter.
We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
arXiv Detail & Related papers (2020-05-25T21:58:09Z) - Russian trolls speaking Russian: Regional Twitter operations and MH17 [68.8204255655161]
In 2018, Twitter released data on accounts identified as Russian trolls.
We analyze the Russian-language operations of these trolls.
We find that trolls' information campaign on the MH17 crash was the largest in terms of tweet count.
arXiv Detail & Related papers (2020-05-13T19:48:12Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.