MSDSI-FND: Multi-Stage Detection Model of Influential Users’ Fake News in Online Social Networks

Abstract

The rapid spread of fake news across social media poses significant threats to politics, economics, and public health. During the COVID-19 pandemic, social media influencers played a decisive role in amplifying misinformation due to their large follower bases and perceived authority. This study proposes a Multi-Stage Detection System for Influencer Fake News (MSDSI-FND) to detect misinformation propagated by influential users on the X platform (formerly Twitter). A manually labeled dataset was constructed, comprising 68 root tweets (42 fake and 26 real) and over 40,000 engagements (26,700 replies and 14,000 retweets) collected between December 2019 and December 2022. The MSDSI-FND model employs a two-stage analytical framework integrating: (1) content-based linguistic and psycholinguistic analysis, (2) user profiles analysis, structural and propagation-based modeling of information cascades analysis. Several machine-learning classifiers were tested under single-stage, two-stage, and full multi-stage configurations. An ablation study demonstrated that performance improved progressively with each added analytical stage. The full MSDSI-FND model achieved the highest accuracy, F1-score, and AUC, confirming the effectiveness of hierarchical, stage-wise integration. The results highlight the superiority of the proposed multi-stage, influential user-aware framework over conventional hybrid or text-only models. By sequentially combining linguistic, behavioral, and structural cues, MSDSI-FND provides an interpretable and robust approach to identifying large-scale misinformation dissemination within influential user-driven social networks.