Detecting Fake Users on Social Media with a Graph Database

  • Yichun Zhao University of Victoria
  • Jens Weber University of Victoria
Keywords: Keywords: fake users; graph database; machine learning; random forest; fake news


Social media has become a major part of people’s daily lives as it provides users with the convenience to connect with people, interact with friends, share personal content with others, and gather information. However, it also creates opportunities for fake users. Fake users on social media may be perceived as popular and influential if not detected. They might spread false information or fake news by making it look real, manipulating real users into making  certain decisions. In computer science, a social network can be treated as a graph, which is a data structure consisting of nodes being the social media users, and edges being the connections between users. Graph data can be stored in a graph database for efficient data analysis. In this paper, we propose using a graph database to achieve an increased scalability to accommodate larger graphs. Centrality measures as features were extracted for the random forest classifier to successfully detect fake users with high precision, recall, and accuracy. We have achieved promising results especially when compared with previous studies. 


Author Biographies

Yichun Zhao, University of Victoria

School of Health Information Science

Department of Human and Social Development

Jens Weber, University of Victoria

Department of Computer Science

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