From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Zhang, Jinghui Wan, Kaiyang Xu, Longwei Li, Ao Liu, Zongfang Chen, Xiuying
Zhang, Jinghui
Wan, Kaiyang
Xu, Longwei
Li, Ao
Liu, Zongfang
Chen, Xiuying
Author
Zhang, Jinghui, Wan, Kaiyang, Xu, Longwei, Li, Ao, Liu, Zongfang, Chen, Xiuying
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for crisis management, policy-making, and social media analysis. However, existing works face notable limitations. First, they lack micro-level personalization, producing generic responses that ignore individual user preferences. Moreover, they overlook macro-level sentiment distribution and only deal with individual-level sentiment, constraining them from analyzing broader societal trends and group sentiment dynamics. To address these challenges, we propose SocialAlign, a unified framework that predicts real-world responses at both micro and macro levels in social contexts. At the micro level, SocialAlign employs SocialLLM with an articulate Personalized Analyze-Compose LoRA (PAC-LoRA) structure, which deploys specialized expert modules for content analysis and response generation across diverse topics and user profiles, enabling the generation of personalized comments with corresponding sentiments. At the macro level, it models group sentiment distributions and aligns predictions with real-world sentiment trends derived from social media data. To evaluate SocialAlign in real-world scenarios, we introduce SentiWeibo, a large-scale dataset curated from authentic social interactions on the Weibo platform. Experimental results on our SentiWeibo and related LaMP benchmark demonstrate that SocialAlign surpasses strong baselines, showing improved accuracy, interpretability, and generalization in public response prediction. We hope our work inspires further research in public response prediction and computational social science: https://github.com/Znull-1220/SocialAlign.
Citation
J. Zhang, K. Wan, L. Xu, A. Li, Z. Liu, and X. Chen, “From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media,” Proceedings of the 33rd ACM International Conference on Multimedia, pp. 5903–5912, Oct. 2025, doi: 10.1145/3746027.3754828
Source
MSMA '25: Proceedings of the 1st International Workshop on Multi-Sensorial Media and Applications
Conference
The 33rd ACM International Conference on Multimedia
Keywords
Public Response Prediction, Sentiment Analysis, Personalization in Social Contexts
Subjects
Source
The 33rd ACM International Conference on Multimedia
Publisher
Association for Computing Machinery