Understanding and identifying the use of emotes in toxic chat on Twitch

Document Type

Article

Publication Date

1-1-2022

Abstract

The latest advances in NLP (natural language processing) have led to the launch of the much needed machine-driven toxic chat detection. Nevertheless, people continuously find new forms of hateful expressions that are easily identified by humans, but not by machines. One such common expression is the mix of text and emotes, a type of visual toxic chat that is increasingly used to evade algorithmic moderation and a trend that is an under-studied aspect of the problem of online toxicity. This research analyzes chat conversations from the popular streaming platform Twitch to understand the varied types of visual toxic chat. Emotes were sometimes used to replace a letter, seek attention, or for emotional expression. We created a labeled dataset that contains 29,721 cases of emotes replacing letters. Based on the dataset, we built a neural network classifier and identified visual toxic chat that would otherwise be undetected through traditional methods and caught an additional 1.3% examples of toxic chat out of 15 million chat utterances.

Identifier

85119059166 (Scopus)

Publication Title

Online Social Networks and Media

External Full Text Location

https://doi.org/10.1016/j.osnem.2021.100180

e-ISSN

24686964

Volume

27

Grant

NRF-2017R1E1A1A01076400

Fund Ref

Ministry of Science, ICT and Future Planning

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