Supervised Learning Models for Social Bot Detection: Literature review and benchmark

Abstract

Over the past decades, online social networks such as Twitter and Facebook have become a significant part of people’s daily lives, particularly amid the ongoing global calamity — the COVID-19 pandemic. This gives room for social bot attacks that are designed to automatically replicate the behavior of real accounts. Most of these bots are employed for nefarious purposes such as disseminating false information, artificially amplifying the popularity of a person or movement, or spreading spam. Many studies have been conducted in an attempt to discover new strategies for identifying social bot accounts. To deal with large-scale attacks from social bots, Machine Learning has emerged as a noticeable path of bot detection problem that can handle massive amounts of data. However, the heterogeneity between studies in terms of problem statements, proposed processes, datasets, and evaluation metrics makes it difficult to assess and compare the efficiency of proposed methods. In this paper, we propose a systematic view of supervised learning methodologies for tweet-based social bot detection, ranging from shallow learning to specific deep learning models. In addition, we introduce a framework that measures various performance aspects and summarizes the in-depth analysis of the results, which were obtained using two datasets comprising 26224 labeled Twitter accounts. The results of this framework, we believe, will be beneficial as a practical guideline for other bot detection research or applications that require the use of machine learning techniques.