DNA-influenced automated behavior detection on twitter through relative entropy

Abstract

Twitter is a renowned microblogging site that allows users to interact using tweets and it has almost reached 206 million daily active users by the second quarter of 2021. The ratio of Twitter bots has risen in tandem with their popularity. Bot detection is critical for combating misinformation and protecting the credibility of online disclosures. Current bot detection approaches rely on the Twitosphere’s topological structure, ignoring the heterogeneity among the profiles. Moreover, most techniques incorporate supervised learning, which depends strongly on large-scale training sets. Therefore, to overcome these issues, we proposed a novel entropy-based framework to detect correlated bots leveraging only user behavior. Specifically, real-time data of users is collected and their online behaviors are modeled as DNA sequences. We then determine the probability distribution of DNA sequences and compute relative entropy to evaluate the distance between the distributions. Accounts with entropy values less than a fixed threshold represent bots. Extensive experiments conducted in real-time Twitter data prove that the proposed detection technique outperforms state-of-the-art approaches with precision = 0.9471, recall = 0.9682, F1 score = 0.9511, and accuracy = 0.9457.