On Using Node Indices and Their Correlations for Fake Account Detection

Paper in proceeding, 2022

With the growing rate of online social networks, the number of fake accounts is multiplying day by day. There exist many approaches in the literature that try to distinguish fake accounts from real ones, for example, those that use machine learning and classification techniques to learn whether a user should be labeled as fake (bot) or not. In this paper, we follow a different approach and try to use node measurements in the field of complex networks analysis to identify fake accounts. We first model users' interactions with a large graph. For example, in Twitter, we can form graphs of follower-following, comments, retweets, mentions, and so on. We then investigate different measurements, such as centrality indices and their correlations, to separate real and fake accounts. We find that measurements such as average path length, eigenvector centrality, harmonic centrality, degree, local reaching centrality and their correlations provide good indicators to distinguish real and fake accounts.