Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web

By G. Hine, J. Onaolapo, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Riginos Samaras, Gianluca Stringhini, Jeremy Blackburn. 2016

246 citations recorded by the source corpus.

The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In particular, /pol/, the “Politically Incorrect'” board, has been a central figure in the outlandish 2016 US election season, as it has often been linked to the alt-right movement and its rhetoric of hate and racism. However, 4chan remains relatively unstudied by the scientific community: little is known about its user base, the content it generates, and how it affects other parts of the Web. In this paper, we start addressing this gap by analyzing /pol/ along several axes, using a dataset of over 8M posts we collected over two and a half months. First, we perform a general characterization, showing that /pol/ users are well distributed around the world and that 4chan's unique features encourage fresh discussions. We also analyze content, finding, for instance, that YouTube links and hate speech are predominant on /pol/. Overall, our analysis not only provides the first measurement study of /pol/, but also insight into online harassment and hate speech trends in social media.

Subfields: Artificial Intelligence, Communication, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Challenges and frontiers in abusive content detection (2019, background): “We propose that anonymity should also be explicitly modelled in future work as it has disinhibiting effects (Amichai-hamburger & McKenna, 2006) and is empirically associated with users posting abuse (Hine et al., 2017).”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “speech and are more likely to contain explicit abuse, such as racism and very intrusive forms of harassment, such as ‘doxxing’ [6, 90, 91].”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “However, more niche platforms, such as Gab or 4chan, tolerate more offensive forms of speech and are more likely to contain explicit abuse, such as racism and very intrusive forms of harassment, such as ‘doxxing’ [55, 73, 120].”
Mean Birds: Detecting Aggression and Bullying on Twitter (2017, background): “Over the past few years, several techniques have been proposed to measure and detect o ensive or abusive content / behavior on platforms like Instagram [25], YouTube [11], 4chan [23], Yahoo”
Why So Toxic?: Measuring and Triggering Toxic Behavior in Open-Domain Chatbots (2022, methodology): “In this work, we use the Politically Incorrect board (/pol/ ) as it is the main board for the discussion of politics/world events and is known for its toxicity [22].”
Chain of Explanation: New Prompting Method to Generate Quality Natural Language Explanation for Implicit Hate Speech (2022, background): “Tremendous hateful speeches are created and spread every second online, which can lead to various social problems [13].”
Community Interaction and Conflict on the Web (2018, background): “Recent research has also shown that organized negative behavior can spread across platforms [33, 82], and that banning communities that foster hate-speech was effective in decreasing hate speech on”