Twitch: Twitch uses a machine learning model combining image analysis and text processing to automatically review custom emote submissions, instantly approving compliant emotes and flagging potential Community Guidelines violations for human review. | AI Trace
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Twitch uses a machine learning model combining image analysis and text processing to automatically review custom emote submissions, instantly approving compliant emotes and flagging potential Community Guidelines violations for human review.
Details
Detailed in a June 2022 Twitch Blog post by Applied Scientist Linda Liu, the system uses MobileNetV2 (pre-trained on ImageNet via transfer learning) for image embeddings and a GRU-based character-level model for emote code text embeddings. These are concatenated and fed through dense layers for multi-class violation classification. Training data includes hundreds of thousands of violating emotes and millions of approved emotes tracked since Q1 2020. The model uses LIME (Local Interpretable Model-agnostic Explanations) for interpretability. It automatically approves a large portion of static emotes, reducing specialist workload and enabling instant emote availability for streamers.