Computational Clickbait Detection: A PSALSAR-based Systematic Literature Review of Methodological Trajectories and Benchmarks (2016–2025)

Abstract

Clickbait headlines have become a persistent issue in online media, where attention often takes priority over informational accuracy. While numerous computational approaches have been proposed to address this issue, the field remains fragmented across datasets, modelling strategies, and linguistic representations. This study presents a systematic literature review of clickbait detection research published between 2016 and 2025, following the Protocol, Search, Appraisal, Synthesis, Analysis, and Report framework to ensure a structured and reproducible review process. A total of 646 records were initially identified from Scopus, IEEE Xplore, Web of Science, and SpringerLink, from which 43 peer-reviewed journal articles were selected for in-depth analysis. Across these studies, a clear shift is evident from traditional feature-based machine learning toward deep learning and transformer-based models. Despite this progression, linguistic cues remain central, either through explicit feature engineering or implicitly learned representations. However, the review reveals a persistent gap: linguistic feature modelling and embedding-based approaches are often developed in isolation. To address this limitation, this study proposes a conceptual framework that integrates language-aware preprocessing, explicit linguistic features, and hybrid embedding strategies within a unified framework. By synthesising these components, the framework offers a more comprehensive perspective on clickbait detection, positioning it as an integrated linguistic and contextual modelling task rather than a purely classification problem. The findings and proposed framework provide a foundation for developing more robust, interpretable, and adaptable clickbait detection systems, particularly in multilingual and low-resource settings.