Navigating Ambiguities: A Systematic Review and Comparative Analysis of Social Bot Detection Methods in Communication Research

The proliferation of social bots poses significant challenges for authentic online communication, motivating a growing body of research in communication studies. Yet, conceptual ambiguities and methodological inconsistencies continue to undermine the reliability and replicability of social bot research. This study reviews recent work on social bots, revealing a frequent mismatch between detection methods and the types of bots being investigated. To address these issues, we propose a typology based on three dimensions: intention (benign vs. malicious), coordination (independent vs. coordinated), and operation (rule-based vs. generative). We further build a multiclass dataset of 4,071 bots and 6,386 humans from Bluesky and evaluate four major detection methods: rule-based approaches, supervised machine learning, unsupervised approaches, and large language model-based techniques. By highlighting the strengths and limitations of each, we advocate for multimethod strategies to better respond to evolving bot behaviors. We conclude with recommendations for standardizing detection practices and enhancing methodological rigor in social bot research.