Detecting Social Media Spambots and Fake Followers Using Explainable Machine Learning Approaches

Abstract

Social networking platforms like X (Twitter) serve as hubs for open human interaction, but they are also increasingly infiltrated by automated accounts masquerading as human users. These bots often engage in activities such as spreading fake news and manipulating public opinion during politically sensitive times like elections. Most of the current bot detection methods rely on black-box algorithms, raising concerns about their transparency and practical usability. This study aims to address these limitations by developing a novel methodology for the detection of spambots and fake followers using annotated data. To this end, we propose an interpretable machine learning (ML) framework, leveraging multiple ML algorithms with hyperparameters optimized through cross-validation, to enhance the detection process. Furthermore, we analyze several features and provide a unique feature set that is optimized to offer excellent performance for bot detection. Moreover, we utilize multiple interpretable AI techniques which include Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). SHAP will help to display the effects of particular characteristics on the model’s prediction which will help in determining whether an account is a bot or a legitimate user. LIME will help to comprehend the model’s predictions, offering clarity regarding the traits or attributes that drive the classification conclusion.