E 2 T 2 : Emote Embedding for T witch T oxicity Detection
Korosh Moosavi, Elias Martin, Muhammad Aurangzeb Ahmad, Afra J. Mashhadi · 2024
The Internet has become the medium of choice for socialization and communication. The rise of live streaming services has created countless online communities of varying sizes with their own jokes, references, slang, and other means of communication. One of the largest live streaming services is Twitch.tv or Twitch, where a unique culture of niche language and emote usage has developed. Emotes are custom-made images, or GIFs, used in chat with varying degrees of access influenced by channel and external site subscription status. Emotes render standard forms of English Natural Language Process- ing (NLP) for tasks such as detection of toxicity or cyberbullying ineffective on Twitch. In this paper, we propose a methodology and offer a largely-trained dataset for detecting emote-based toxicity on live streaming platforms such as Twitch.