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.

A language framework for modeling social media account behavior

“…We compared the performance of the BLOC model to three baseline models: Botometer-V4 (the current version of Botometer at the time of writing) [18] and two DNA-based methods, namely DDNA [27,28] and DNA-influenced [53]. The latter were selected because they share some similarities with BLOC.…”

“…The method relies on a formula to calculate a probability distribution for a given string, and on the symmetrized KL divergence to calculate the distance between the probability distributions associated with two strings [55]. In this way, the method calculates the distance between the DDNA strings corresponding to two accounts [53]. To implement this method, we partitioned the bot accounts in the training dataset into groups of 50, similar to Gilmary et al [53].…”

A General Language for Modeling Social Media Account Behavior

“…We compared the performance of BLOC to three baseline models: Botometer-V4 (the current version of Botometer at the time of writing) [43] and two DNA-based methods, namely DDNA [12,10] and DNA-influenced [23]. The latter were selected because they share some similarities with BLOC.…”

“…The method relies on a formula to calculate a probability distribution for a given string, and on the symmetrized KL divergence to calculate the distance between the probability distributions associated with two strings [58]. In this way, the method calculates the distance between the DDNA strings corresponding to two accounts [23]. To implement this method, we partitioned the bot accounts in the training dataset into groups of 50, similar to Gilmary et al [23].…”

“…In this way, the method calculates the distance between the DDNA strings corresponding to two accounts [23]. To implement this method, we partitioned the bot accounts in the training dataset into groups of 50, similar to Gilmary et al [23]. For each Table 2: Annotated datasets used in our bot detection evaluation.…”