The Impact of Social Media on Political Narratives
TL;DR
The research proposes computational indicators such as the Narrative Amplification Index (NAI), Sentiment Dominance Ratio (SDR), and Polarization Coefficient to measure narrative influence and provides computational insights into digital political ecosystems.
Abstract
The rapid growth of social media has transformed political communication by decentralizing information flows and replacing traditional gatekeeping with algorithm-driven content distribution. This shift has significantly influenced democratic participation, opinion formation, electoral mobilization, polarization, and information integrity. This study examines how political narratives are created and amplified through user-generated content, algorithmic curation, and network dynamics. Using a hybrid methodology—combining content analysis, sentiment modeling, network centrality measures, and engagement metrics—the research proposes computational indicators such as the Narrative Amplification Index (NAI), Sentiment Dominance Ratio (SDR), and Polarization Coefficient (PC) to measure narrative influence. Analysis of social media posts during a major electoral cycle reveals that algorithmic amplification strengthens ideological homogeneity and increases cross-group polarization. Approximately 68% of highly engaged political posts contained emotionally charged framing, highlighting the role of affective language in narrative virality. Coordinated networks also contributed significantly to rapid narrative diffusion. The findings demonstrate that social media platforms actively shape political discourse through engagement-driven algorithms, prioritizing emotionally intense and polarizing content. The study provides computational insights into digital political ecosystems and offers implications for policymakers and platform regulators seeking to balance free expression with information integrity