Probabilistic Inference on Twitter Data to Discover Suspicious Users and Malicious Content

While the power of social media on the Internet is undeniable, it has become a major weapon for launching cyberattacks against an organization and its people. Today, there is a growing number of cyberattacks being launched through social media such as posting of false content from hacked accounts, posting malicious URLs to spread malware, and others. In this paper, we present a simple and flexible unified framework called SocialKB for modeling social media posts and reasoning about them to ascertain their veracity, a first step towards discovering emerging cyber threats. SocialKB is based on Markov Logic Networks (MLNs), a popular representation in statistical relational learning. It learns a knowledge base (KB) on the social media posts and users' behavior in a unified manner. By conducting probabilistic inference on the KB, SocialKB can identify suspicious users and malicious content. In this work, we specifically focus on tweets posted by users on Twitter. Finally, we report an evaluation of SocialKB on 20,000 tweets and discuss our early inference results.