PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media

TL;DR

PERCEIVE is a comprehensive bilingual benchmark that captures personalized emotional responses and communication behaviors on social media, integrating social context and user attributes for improved emotion analysis.

Contribution

It introduces the first large-scale, multi-dimensional social perception benchmark that includes genuine reader feedback, communication intent, and social graph data.

Findings

State-of-the-art models underperform on this multifaceted, user-aware task.

PERCEIVE reveals significant gaps in current emotion analysis approaches.

The benchmark enables more personalized and socially-aware emotion understanding.

Abstract

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.