LIN: Latent Influence Network for Discovering Hidden Directed Influence Links on Social Media

Abstract

In the current social media landscape, the study of influence propagation and consensus formation has gained prominence. While user interactions like retweeting are apparent, the underlying pathways of influence often remain hidden and complex. This study proposes a novel network called Latent Influence Network (LIN), which advances the analysis of influence on social media. LIN's architecture and the process of parameter selection are meticulously discussed within the comprehensive Latent Influence Detection Framework (LIDET). Based on the user's behavior label, LIN identifies the optimal network configuration, revealing more accurate influence patterns. We applied the LIDET framework to four diverse datasets, each demonstrating substantial improvements in influence pattern recognition over traditional network models. Specifically, in a case study on a COVID-19 dataset, LIN achieved a classification accuracy of 99%, significantly outperforming conventional methods. These findings underscore the utility of LIN in capturing the dynamics of influence and enhancing our understanding of opinion formation on social media.