Modeling Temporal Activity to Detect Anomalous Behavior in Social Media

Abstract

Social media has become a popular and important tool for human communication. However, due to this popularity, spam and the distribution of malicious content by computer-controlled users, known as bots, has become a widespread problem. At the same time, when users use social media, they generate valuable data that can be used to understand the patterns of human communication. In this article, we focus on the following important question: Can we identify and use patterns of human communication to decide whether a human or a bot controls a user? The first contribution of this article is showing that the distribution of inter-arrival times (IATs) between postings is characterized by following four patterns: (i) heavy-tails, (ii) periodic-spikes, (iii) correlation between consecutive values, and (iv) bimodallity. As our second contribution, we propose a mathematical model named Act-M (Activity Model). We show that Act-M can accurately fit the distribution of IATs from social media users. Finally, we use Act-M to develop a method that detects if users are bots based only on the timing of their postings. We validate Act-M using data from over 55 million postings from four social media services: Reddit, Twitter, Stack-Overflow, and Hacker-News. Our experiments show that Act-M provides a more accurate fit to the data than existing models for human dynamics. Additionally, when detecting bots, Act-M provided a precision higher than 93% and 77% with a sensitivity of 70% for the Twitter and Reddit datasets, respectively.