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Quantifying Truthfulness: A Probabilistic Framework for Atomic Claim-Based Misinformation Detection

Author

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  • Fahim Sufi

    (School of Public Health and Preventive Medicine, Monash University, Australia, VIC 3004, Australia)

  • Musleh Alsulami

    (Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21961, Saudi Arabia)

Abstract

The increasing sophistication and volume of misinformation on digital platforms necessitate scalable, explainable, and semantically granular fact-checking systems. Existing approaches typically treat claims as indivisible units, overlooking internal contradictions and partial truths, thereby limiting their interpretability and trustworthiness. This paper addresses this gap by proposing a novel probabilistic framework that decomposes complex assertions into semantically atomic claims and computes their veracity through a structured evaluation of source credibility and evidence frequency. Each atomic unit is matched against a curated corpus of 11,928 cyber-related news entries using a binary alignment function, and its truthfulness is quantified via a composite score integrating both source reliability and support density. The framework introduces multiple aggregation strategies—arithmetic and geometric means—to construct claim-level veracity indices, offering both sensitivity and robustness. Empirical evaluation across eight cyber misinformation scenarios—encompassing over 40 atomic claims—demonstrates the system’s effectiveness. The model achieves a Mean Squared Error (MSE) of 0.037, Brier Score of 0.042, and a Spearman rank correlation of 0.88 against expert annotations. When thresholded for binary classification, the system records a Precision of 0.82, Recall of 0.79, and an F1-score of 0.805. The Expected Calibration Error (ECE) of 0.068 further validates the trustworthiness of the score distributions. These results affirm the framework’s ability to deliver interpretable, statistically reliable, and operationally scalable misinformation detection, with implications for automated journalism, governmental monitoring, and AI-based verification platforms.

Suggested Citation

  • Fahim Sufi & Musleh Alsulami, 2025. "Quantifying Truthfulness: A Probabilistic Framework for Atomic Claim-Based Misinformation Detection," Mathematics, MDPI, vol. 13(11), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1778-:d:1665426
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    References listed on IDEAS

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    1. Fahim Sufi, 2024. "Advances in Mathematical Models for AI-Based News Analytics," Mathematics, MDPI, vol. 12(23), pages 1-22, November.
    2. Junsol Kim & Zhao Wang & Haohan Shi & Hsin-Keng Ling & James Evans, 2025. "Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
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