An Innovative Method for Detecting Fake News Distribution Sources based on Machine Learning Technology and Graph Theory

An innovative approach to identifying rapidly spreading false information is to create a targeted graph and its subsequent clustering. A method for detecting rapidly spreading fake messages in social networks has been developed. K-means, Louvain, and Leiden algorithms were applied to identify large communities in graphs, enabling the rapid detection of fake news. A modified fake news detection algorithm based on k-means and Leiden can group fake news clusters, enabling rapid identification of widely spreading news. The combination of Leiden for structural analysis of communities and SVM for classification provides an optimal balance between accuracy (F1-score = 0.87) and completeness of fake detection (Recall = 97%), allowing the system to be used both for analysing large datasets and for monitoring new publications. The Lei-den algorithm demonstrated the highest modularity (Q = 0.7212), which is 4.8% better than Louvain (Q = 0.6884), and detected 40 structural communities. The modified method has a lower modularity (Q = 0.5584), since modularity is not calculated for K-means.