Rumor Detection Based on Macro-Micro Public Opinion Modeling and Sentiment Scores

Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-has received insufficient attention. We propose a macro-micro based public opinion (MMPO) modeling method to address these gaps. This approach enables a better understanding of news dissemination on social media platforms. Firstly, we incorporate post sentiment as a crucial feature. Secondly, we examine how the propagation at the micro-level influences sub-posts. Next, we investigate the temporal variations in public opinion from a macro perspective. Lastly, we integrate the acquired general propagation information with textual data to obtain more distinct representations of news propagation. By leveraging the macro perspective, the proposed model effectively captures the fine-grained characteristics of news propagation and helps smooth out the noise in user comments. The experimental evaluation showcases the superiority of our model in rumor detection compared to existing methods.