Dynamics of competing public sentiment contagion in social networks incorporating higher-order interactions during the dissemination of public opinion

The escalating intricacies of public interactions on social media have markedly intensified the challenges associated with public opinion management. This investigation delves into the intricate mechanisms of public sentiment contagion, aiming to offer practical insights to mitigate the adverse impacts of public opinion. In this study, we introduce a competing public sentiment contagion model that incorporates higher-order interactions, considering four heterogeneous groups. First, at the theoretical level, we unveil the competing dynamics of individual emotional state conversions, taking account into homogenization effects through the Microscopic Markov Chain Approach. Second, at the practical level, our model elucidates the effective factors for governing public sentiment contagion on social networks. Noteworthy findings indicate that population scale, structural intricacy, and initial infection count have no influence on the contagion dynamics. Instead, the composition of the population and the ability of emotional contagion emerge as pivotal determinants. Validation within real-world social networks confirms the efficacy of our model. This study strives to provide effective methodologies for guiding public sentiment and exerting control over public opinion, holding significance both in theoretical and practical contexts.