Minimizing Influence of Rumors by Blockers on Social Networks
Keywords:
Blocker deployment, Centrality metrics, Cybersecurity, Digital trust, Graph theory, Influence propagation, Java framework, Machine learning, Misinformation mitigation, Real-time visualization, Rumor detection, Social network analysisAbstract
The widespread use of social media has reshaped how people communicate and consume information. However, these platforms are also vulnerable to the rapid spread of rumors and misinformation. This paper introduces a proactive system that aims to limit the influence of such false information through the strategic use of "blockers" key nodes in the network positioned to intercept and reduce rumor diffusion.
The system conceptualizes a social network as a graph, where users are represented as nodes and interactions as edges. Using centrality algorithms, influence propagation models, and machine learning techniques, the system identifies influential nodes and determines optimal points for blocker placement. A predictive AI component analyzes historical rumor patterns to improve deployment accuracy dynamically.
Built using Java, the platform includes a real-time visualization interface that allows administrators to monitor spread dynamics, assess blocker effectiveness, and adjust strategies on the fly. Designed for scalability and adaptability, the framework contributes to ongoing efforts in misinformation control, social network analysis, and digital trust.
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