Machine Learning-Based Detection of Misinformation in Social Media Communication Networks

Niloofar Rahimi

Machine Learning-Based Detection of Misinformation in Social Media Communication Networks

Author(s)	Niloofar Rahimi
Country	Iran
Abstract	Social media communication networks have transformed the production, circulation, and consumption of information by enabling users to exchange messages rapidly across interconnected digital communities. Although these platforms support information accessibility and public communication, they also facilitate the circulation of inaccurate and misleading factual claims. The identification of misinformation presents significant computational challenges because message content is frequently brief, contextual information may be incomplete, and patterns of information dissemination vary across users and communication networks. Conventional approaches based exclusively on textual characteristics may overlook potentially informative relationships between messages, interaction patterns, and the temporal development of online discussions. The present paper develops a hypothetical methodological framework for examining machine learning-based misinformation detection in social media communication networks. The proposed investigation integrates textual feature analysis, network interaction characteristics, temporal propagation indicators, and supervised classification. Four computational approaches are considered: TF-IDF-based Logistic Regression, Linear Support Vector Machine, Random Forest with content and network features, and Gradient Boosting with integrated textual, structural, and temporal information. A fictional dataset containing 12,000 social media messages associated with 600 independently verified factual claims is introduced to demonstrate the intended research design. Two illustrative analytical tables describe the hypothetical distribution of verified accurate and false claims and the comparative classification performance of the selected models. Precision, recall, and F1-score are considered the principal performance measures. All numerical values are manually constructed and do not represent collected social media records, trained classification models, or independently verified experimental outcomes. The methodological discussion examines claim verification, linguistic ambiguity, network dependency, label uncertainty, temporal data leakage, and responsible model interpretation. Particular attention is given to distinguishing misinformation from unverified rumors and avoiding unsupported assumptions concerning the intentions of individual users. The paper concludes that machine learning provides a relevant analytical foundation for misinformation detection, although trustworthy implementation requires reliable reference evidence, representative datasets, independent evaluation, and appropriate human review.
Keywords	Misinformation Detection, Social Media Networks, Machine Learning, Information Credibility, Natural Language Processing, Network Analysis, Rumor Verification, Gradient Boosting, Digital Communication, Classification Models.
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2026
Published On	2026-02-27

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International Journal of Machine Learning, AI & Data Science Evolution

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Machine Learning-Based Detection of Misinformation in Social Media Communication Networks

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