Computational approaches to controversy detection: a systematic review

The prevalence of controversies in online interactions has recently led researchers to develop diverse computational methods for controversy detection, reflecting the many ways controversies manifest across contexts. This paper presents a systematic literature review of computational methods for Controversy Detection to provide a unified framework for this rapidly evolving field. To handle the diversity of approaches, we introduce an inclusive operational definition that accommodates various manifestations while maintaining analytical precision. Building on this foundation, we gathered 47 studies spanning 2010–2023. Our analysis yields three main outcomes: (1) a detailed inventory of datasets used in controversy detection; (2) a six-dimensional taxonomy of linguistic and interactive features for operationalizing the task; and (3) an evaluation of computational models based on these features, showing that deep learning models integrating linguistic attributes with graph-based user interaction representations achieve superior performance. Despite these advances, real-world deployment remains limited by several gaps: conceptualization issues, an over-reliance on Twitter at the expense of other platforms, and technical challenges particularly around explainability. The paper concludes by identifying future research directions to address these shortcomings.