InfoSec Seminar: "Go back to Reddit!": Detecting Hate and Analyzing Narratives of Online Fringe Communities

Speaker: Antonis Papasavvas

Date/Time: 12-Nov-2020, 00:00 UTC




Modern society increasingly relies on the Internet for a wide range of tasks, including gathering, sharing, and commenting on content, events, and discussions. Alas, the Web has also enabled anti-social and toxic behavior to occur at an unprecedented scale. Malevolent actors routinely exploit social networks to target other users via hate speech and abusive behavior or spread extremist ideologies and conspiracy theories. A non-negligible portion of these nefarious activities often originates on “fringe” online platforms, e.g., 4chan, 8chan, Gab, Voat. 

Online fringe communities offer fertile grounds for users to seek and share paranoid ideas fueling suspicion of mainstream news, and outright conspiracy theories. Among these, the QAnon conspiracy theory has emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. At the same time, governments are thought to be controlled by “puppet masters,” as democratically elected officials serve as a fake showroom of democracy.

The first part of this talk presents a dataset with over 3.3M threads and 134.5M posts from the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted over a period of almost 3.5 years (June 2016-November 2019). In this work, we present a statistical analysis of the dataset, in addition to content analysis, shedding light on the most prominent discussion topics, the most popular entities mentioned, and the toxicity level of each post. 

The second part of the talk provides an empirical exploratory analysis of the QAnon community on, a Reddit-esque news aggregator, which has recently captured the interest of the press for its toxicity and for providing a platform to QAnon followers. More precisely, we analyze a large dataset from /v/GreatAwakening, the most popular QAnon-related subverse (the Voat equivalent of a subreddit) to characterize activity and user engagement. To further understand the discourse around QAnon, we study the most popular named entities mentioned in the posts, along with the most prominent topics of discussion. We also use word2vec models to identify narratives around QAnon-specific keywords and analyze content toxicity of the discussions on /v/GreatAwakening and the broad Voat community.


I am a PhD researcher at University College London under the supervision of Prof. Emiliano De Cristofaro and Dr. Enrico Mariconti focusing on the characterization and detection of racism, misogyny, and other types of discriminating and extreme behavior in mainstream and non-mainstream online social networks, large scale data processing, and deep learning networks.

In 2019 I received my master's degree in Data Science and Engineering from the Cyprus University of Technology, where I also spent 3 years working as a research fellow. My research during my master focused on device-centric authentication, federated identity management, cybersafety, and the detection and characterization of inappropriate content online. Also, I had the opportunity to collaborate with various academic and industrial institutions across the EU, while engaged in multiple EU-funded projects. Specifically, I took over highly responsible roles and actively contributed to ReCRED and ENCASE projects as a pilot manager and technical lead, respectively. In addition, I had the opportunity to spend 8 months as a visiting researcher at Telefonica I+D, Barcelona, Spain. My research during that internship resulted in a publication that was honorably mentioned during the 14th AAAI International Conference on Web and Social Media, 2020.



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