PERCEIVE is the first bilingual benchmark integrating author content, reader emotions from comments, communication behavior, user attributes, and social graphs for personalized social media emotion understanding.
arXiv preprint arXiv:2402.12150 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.
citing papers explorer
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PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media
PERCEIVE is the first bilingual benchmark integrating author content, reader emotions from comments, communication behavior, user attributes, and social graphs for personalized social media emotion understanding.
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.