Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

Abstract

Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication, such as cyberbullying and harassment. Nevertheless, detecting online toxicity is challenging due to its multi-dimensional, context sensitive nature. As exposure to online toxicity can have serious social consequences, reliable models and algorithms are required for detecting and analyzing such communication across the vast and growing space of social media. In this paper, we draw on psychological and social theory to define toxicity. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.

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Cited by in corpus (5)

• Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities
• The influence of coordinated behavior on toxicity
• On mission Twitter Profiles: A Study of Selective Toxic Behavior
• Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users
• Tracking Patterns in Toxicity and Antisocial Behavior Over User Lifetimes on Large Social Media Platforms