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This paper proposes a zero-shot detection pipeline to characterize human-likeness in AI-generated poetry, aiming to identify attributes that distinguish human from machine poems and improve detection methods.
This paper uses a pre-trained LLM with zero-shot classification to analyze approximately 20 million Twitch chat messages across seven game genres, finding that 2.4% of messages are toxic, with MOBA games having the highest rate (3.2%) and sports games the lowest (2%). The study also identifies significant differences in toxicity distributions across individual games within the same genre.