PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media
For NLP researchers, this benchmark provides a foundational resource to advance socially-intelligent models that understand emotion and behavior in social media context.
The paper introduces PERCEIVE, a bilingual benchmark integrating author content, reader emotional feedback, communication behavior, user attributes, and social graphs to enable personalized, reader-centric emotion analysis. Evaluation of state-of-the-art methods, including LLMs, reveals significant shortcomings in handling this multifaceted task.
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.