Dynamic Analysis of Influencer Impact on Opinion Formation in Social Networks

The rapid proliferation of social media platforms has transformed communication, enabling individuals to share opinions and influence others on an unprecedented scale. This paper addresses the challenge of quantifying the ability of social media influencers to change opinions over time. Traditional metrics, such as follower counts or engagement rates, offer a limited view of an influencer’s true impact. To face this challenge, this study provides a nuanced framework based on Friedkin-Johnsen model and Sentiment Analysis for analyzing how people’s opinions propagate through social networks and how influencers can affect these dynamics. The methodology consists in building interaction network graphs, detecting communities, and identifying key influencers using classic topology metrics. Then, it applies Sentiment Analysis to capture users’ opinions, which are injected into the Friedkin-Johnsen model to study their evolution over time. The results show the effectiveness of the proposed approach in determining the dynamics of social influence and opinion change.