Machine learning models using text, user, and network attributes classify Twitter users as bullies or aggressors with over 90% accuracy and AUC.
Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
fields
cs.SI 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Detecting Cyberbullying and Cyberaggression in Social Media
Machine learning models using text, user, and network attributes classify Twitter users as bullies or aggressors with over 90% accuracy and AUC.