PERCEIVE：面向社交媒体个性化情绪与交流行为理解的基准测试
PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media
AI总结：
PERCEIVE是首个整合五维社交感知的双语大规模基准，通过真实用户互动捕捉读者个性化情绪响应，推动社交智能NLP研究。
摘要：
当前社交媒体情感分析多以作者为中心，未能捕捉不同读者的主观情绪反应。本文提出PERCEIVE，首个整合作者内容、读者反馈、交流行为、用户属性及社交图谱的双语基准，通过标注读者评论情绪和同步捕捉交流意图，为建模情绪与行为的内在耦合提供独特资源。评估协议测试了先进方法，揭示现有方法在处理多维用户感知任务中的不足，为社交智能NLP未来研究奠定基础。
英文摘要：
Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diverse readers. This paradigm overlooks the crucial link between individual perception, communication behavior, and the underlying social network. To bridge this gap, we introduce PERCEIVE, a novel bilingual (English and Chinese) large-scale benchmark that, to the best of our knowledge, is the first to integrate five critical dimensions for social perception: author-created content, genuine readers' emotional feedback (derived from their comments), communication behavior, user attributes, and the social graph. This benchmark enables a paradigm shift towards truly personalized, reader-centric analysis, where different readers' emotional responses to the same content are naturally captured through their real-world interactions. By annotating emotions from reader comments and synchronously capturing communication intent, PERCEIVE provides a unique resource to model the intrinsic coupling between emotion and behavior, grounded in social context. We establish a comprehensive evaluation protocol, testing state-of-the-art methods, including large language models (LLMs) with advanced reasoning enhancement. Our findings reveal significant shortcomings in existing approaches when handling this multifaceted, user-aware task. PERCEIVE offers a foundational resource and clear direction for future research in socially-intelligent NLP, pushing models towards a more unified understanding of emotion on social media.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
