The study delves into infodemics in bounded-confidence content spread models on networks. It defines an opinion reproduction number to determine infodemic thresholds and analyzes various network parameters' impact on content spread. The research investigates dissemination tree properties like size, width, longest adoption paths, and structural virality.
The study examines how network size, mean degree, receptiveness parameter, and initial content state influence the total number of content shares and dissemination patterns. Larger networks and higher receptiveness promote wider content spread. Varying the expected mean degree affects dissemination tree statistics differently.
Future directions include exploring purposeful source node selection for influence maximization and studying competing social contagions' effects. Incorporating content mutation and distributed reproduction numbers are potential extensions. Further investigations could involve complex network structures like multilayer networks or hypergraphs.
Software used for computations was MATLAB, with code available for reproducibility at a provided GitHub link. Funding acknowledgments are made to NSF grants supporting the authors' work.
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by Heather Z. B... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01066.pdfDeeper Inquiries