Identification of areas of influence using a thematic modeling approach and belief function theory

Abstract

Social networks have become essential platforms for the dissemination of information and the exchange of ideas. They are transforming the way influencers interact and exert influence over their peers. On platforms such as Twitter, Facebook and Instagram, influence is expressed through social interactions such as retweets, likes, mentions, comments and shares. These activities play a key role in amplifying messages and shaping opinions. Studying the practices of influencers allows for a more precise identification of the domains in which their impact is particularly significant. However, this task is complex. It requires rigorous methods capable of integrating various forms of social engagement while managing the uncertainties associated with heterogeneous data. In this context, we propose a method to identify the domain of influence of a social media influencer. This approach combines thematic modeling Latent Dirichlet Allocation (LDA) with Belief Function Theory (BFT) to analyze social interactions and dominant topics of interest. By incorporating indicators such as retweets, likes and mentions, the method provides a robust framework for evaluating the influencer's impact across different domains. It thus offers precise tools for researchers, practitioners and decision-makers aiming to better understand these complex dynamics.