EmoWeave: Towards Emotional Support Conversation on Social Media

Social media users share emotional experiences that may call for support without an explicit request for help. We formulate Social Media Emotional Support Conversation (SM-ESC), in which a model provides support through public comments and adapts to the poster's feedback. Evaluating new replies requires poster reactions that archived exchanges cannot provide. We introduce EmoWeave, a benchmark of 500 text and image cases grounded in FDU-CommentR Weibo posts. Its three-perspective protocol combines supporter replies based on public evidence, poster reactions simulated from private profiles, and independent bystander judgments of quality and safety. Source evidence grounds these profiles; historical replies and human review assess compatibility and simulation fidelity. Across nine models, automated safety warnings occur in 2.8–6.6% of completed text interactions and can coexist with positive simulated emotion gain. External leaderboard rankings only partly align with support performance. Training with interactive feedback improves Qwen2.5-7B-Instruct's emotional outcomes and response quality while reducing warnings on held-out text cases. The benchmark and code are available at https://anonymous.4open.science/r/EMOWEAVE-6EBF.