International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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A REVIEW OF TEMPORAL MISINFORMATION SPREAD FORECASTING IN ONLINE SOCIAL NETWORKS USING GRAPH-STRUCTURED DEEP LEARNING MODELS Manish Kumar Pandey1, Mrs. Arifa Khan2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rapid proliferation of misinformation in
propagation of misinformation—defined as false or misleading information shared irrespective of intent to deceive. Empirical studies demonstrate that misinformation spreads faster and reaches broader audiences than factual information due to novelty effects, emotional appeal, and algorithmic amplification (Vosoughi, Roy and Aral, 2018). The structural characteristics of OSNs, including scale-free connectivity and community clustering, further accelerate cascade formation and viral diffusion processes (Barabási and Albert, 1999). Consequently, misinformation diffusion has become a critical interdisciplinary research problem spanning computer science, sociology, and information systems.
online social networks has emerged as a critical societal challenge, influencing public opinion, electoral processes, and public health responses. Accurate forecasting of temporal misinformation spread is therefore essential for early intervention and mitigation strategies. Recent advances in graph-structured deep learning have provided powerful tools for modeling the complex interplay between network topology and temporal dynamics inherent in information diffusion. This review systematically synthesizes existing research on temporal misinformation spread forecasting using graphbased deep learning models. We present a structured taxonomy covering static graph neural networks with temporal features, dynamic graph neural networks, hybrid GNN–sequence architectures, and spatio-temporal graph models. The review critically examines methodological designs, benchmark datasets, evaluation protocols, and reported performance trends. Furthermore, we analyze key challenges, including scalability to large-scale networks, handling temporal irregularity, data imbalance, robustness against adversarial manipulation, and model interpretability. By consolidating fragmented research across diffusion modeling and graph representation learning, this review highlights emerging directions such as multimodal fusion, cross-platform forecasting, and explainable graph intelligence. The paper aims to provide researchers and practitioners with a comprehensive understanding of current capabilities, limitations, and future opportunities in temporal misinformation spread forecasting.
1.1.1 Impact on Society, Politics, and Public Health The societal consequences of misinformation are substantial and multidimensional. In political contexts, coordinated misinformation campaigns have been linked to electoral manipulation and polarization (Allcott and Gentzkow, 2017). During public health crises such as the COVID-19 pandemic, misinformation undermined trust in scientific guidance and vaccination programs, exacerbating global health risks (Cinelli et al., 2020). The rapid online amplification of rumors and conspiracy theories has also been associated with social unrest and economic instability. These impacts underscore the need not only for detection but also for proactive forecasting mechanisms capable of anticipating diffusion trajectories before misinformation reaches critical mass. 1.1.2 Relevance of Forecasting Misinformation Spread
Key Words: Temporal Misinformation Forecasting; Graph Neural Networks; Dynamic Social Networks; Information Diffusion Modeling; Spatio-Temporal Deep Learning; Online Social Media Analytics
Traditional misinformation research has focused primarily on classification and detection after dissemination has occurred. However, early-stage forecasting offers a preventive paradigm by predicting cascade growth, diffusion speed, and eventual reach. Forecasting enables platform moderators and policymakers to allocate intervention resources efficiently and implement timely countermeasures. From a computational perspective, misinformation spread forecasting is inherently a spatiotemporal prediction problem, where future cascade states must be inferred from evolving network interactions and historical propagation patterns (Cheng et al., 2014).
1. INTRODUCTION 1.1 Background on Misinformation in Online Social Networks Online social networks (OSNs) such as Twitter, Facebook, and Weibo have fundamentally transformed the mechanisms of information production and dissemination. While these platforms enable rapid communication and democratized content creation, they also facilitate the large-scale
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