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Hot Topic Propagation Model Based on Short-Term Emotional and Behavioral Differences

Bibliographic Data

ID22108758
AuthorsChaolong Jia (0000-0003-4595-8215, Chongqing University of Posts and Telecommunications), Zigao Huang (0009-0007-5183-2551, Chongqing University of Posts and Telecommunications), Lian Zou (0009-0009-2985-3123, Chongqing University of Posts and Telecommunications), Guicai Deng (0009-0006-6500-8996, Chongqing University of Posts and Telecommunications), Siyan Huang (0009-0005-3450-288X, Chongqing University of Posts and Telecommunications), Hongjun Zhu (0000-0003-2339-8739, Chongqing University of Posts and Telecommunications), Tun Li (0000-0002-7190-0167, Chongqing University of Posts and Telecommunications)
Year2026
Volume13
Issue3
Pages3850-3862
Publication date2026-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2026.3656189
OpenAlexW7133223976
LanguageEN
References cited30

The spread of hot topics in social networks exhibits strong contagiousness, reflecting the interplay between emotional resonance and social influence. Understanding this mechanism is crucial for explaining collective online behavior and improving information diffusion management. This study proposes a hot topic propagation model based on short-term emotions and behavioral differences. Traditional sentiment analysis methods rely excessively on single-text data. To address this limitation, the proposed model quantifies the matching degree between user emotions and topic content through nonlinear functions. In addition, it constructs sentiment influence factors from multimodal information to reveal the emotional mechanisms of communication. Considering the uncertainty of user behavior and the driving effect of group influence, an intention-driving model grounded in communication psychology is developed to jointly describe individual and collective behavioral tendencies. Furthermore, a behavioral difference-based propagation model, SCOIR, is established to characterize the temporal dynamics and behavioral complexity of topic diffusion through information entropy and a reward matrix. Experimental results demonstrate that the proposed model effectively captures users’ emotional dynamics and behavioral patterns, achieving accurate simulation of topic propagation. Overall, this work introduces a multimodal sentiment influence modeling approach, an intention-driving mechanism based on communication psychology, and a behaviorally heterogeneous propagation model SCOIR, providing both theoretical insights and practical implications for understanding emotion-driven information diffusion

Behavioral modeling · Behavioural sciences · Collective behavior · Cybernetics · Sentiment analysis · Topic model · Advanced Computing and Algorithms · Complex Network Analysis Techniques · Opinion Dynamics and Social Influence

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