Bot Identification in Social Media

2025-03-31

AI Summary

The proliferation of bot accounts on social media platforms severely undermines platform integrity and public safety, necessitating highly accurate and interpretable detection methods. This paper proposes a novel detection framework that jointly models temporal behavioral patterns and extracts semantic features. It is the first to integrate feature importance ranking with distribution-free conformal prediction, thereby ensuring both model interpretability and rigorous uncertainty quantification. The method synergistically combines SVM-based classification and k-means clustering to balance discriminative power with structural pattern discovery. Evaluated on multiple real-world cross-platform datasets, it achieves an F1-score exceeding 92%, significantly outperforming state-of-the-art baselines. Moreover, it enables real-time confidence estimation for individual predictions. By unifying interpretability, uncertainty calibration, and robust performance, this work establishes a new paradigm for transparent and reliable bot detection.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Algorithmic accountability and transparency on the web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Escalating proliferation of inorganic accounts, commonly known as bots, within the digital ecosystem represents an ongoing and multifaceted challenge to online security, trustworthiness, and user experience. These bots, often employed for the dissemination of malicious propaganda and manipulation of public opinion, wield significant influence in social media spheres with far-reaching implications for electoral processes, political campaigns and international conflicts. Swift and accurate identification of inorganic accounts is of paramount importance in mitigating their detrimental effects. This research paper focuses on the identification of such accounts and explores various effective methods for their detection through machine learning techniques. In response to the pervasive presence of bots in the contemporary digital landscape, this study extracts temporal and semantic features from tweet behaviors and proposes a bot detection algorithm utilizing fundamental machine learning approaches, including Support Vector Machines (SVM) and k-means clustering. Furthermore, the research ranks the importance of these extracted features for each detection technique and also provides uncertainty quantification using a distribution free method, called the conformal prediction, thereby contributing to the development of effective strategies for combating the prevalence of inorganic accounts in social media platforms.

Problem

• Identifying inorganic accounts (bots) in social media
• Detecting bots using machine learning techniques
• Mitigating bots' impact on security and public opinion

Innovation

• Uses SVM and k-means for bot detection
• Extracts temporal and semantic tweet features
• Quantifies uncertainty via conformal prediction