STRATEGIC MANAGEMENT OF SOCIAL MEDIA INFLUENCE NETWORKS FOR ENHANCED LOCAL GOVERNANCE AND COMMUNITY ENGAGEMENT

Abstract

Social media has emerged as a critical tool for local governments and public institutions to engage citizens, disseminate information, and shape public opinion. This study proposes a graph analytics approach to quantify influence and optimize communication strategies within social networks, emphasizing local governance and community engagement. By employing network centrality metrics such as degree, betweenness, closeness, and eigenvector centrality, the research identifies key opinion leaders and influential nodes that significantly impact information diffusion and citizen engagement. The proposed model enables local authorities to prioritize communication efforts, enhance public outreach, and improve policy dissemination. Empirical validation using datasets from municipal social media platforms demonstrates that targeted engagement strategies informed by network analytics lead to higher interaction rates, increased citizen participation, and more effective community-focused campaigns. The findings highlight the potential of data-driven social media strategies to strengthen local governance, enhance transparency, and foster participatory democracy.