Development of a Social Network Analysis Application to Identify Influential Subgroups of Networks and Users Involved in Spreading Negative Issues

This study focuses on developing a Social Network Analysis (SNA) tool to assist the Directorate of Information Security and Control Operations (D221) under the National Cyber and Crypto Agency (BSSN) of Indonesia in monitoring social media, particularly Twitter, for emerging and potentially harmful issues. The increasing number of Twitter users has led to a rise in social conflicts stemming from misleading or offensive content. Currently, D221 uses separate tools for data retrieval and visualization, which complicates the monitoring process and lacks clarity in centrality analysis. To address these limitations, this research adopts the Design Research Methodology (DRM) and incorporates a structured SNA framework using Python-based libraries such as Igraph and SNScrape, and the Flask web framework. The tool enables data collection, graph visualization, and centrality calculations (degree, betweenness, closeness, and eigenvector) within a single system. The developed platform underwent functional and user acceptance testing, demonstrating its effectiveness in visualizing user interactions and identifying influential actors in online discussions, thereby supporting BSSN’s strategic operations in securing cyberspace.