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FANE: A FAke NEws Detector Based on Syntactic, Semantic, and Social Features Bayesian Analysis

Author

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  • Varsha Arya

    (Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India)

  • Razaz Waheeb Attar

    (Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia)

  • Ahmed Alhomoud

    (Department of Computer Science, Faculty of Science, Northern Border University, Arar, Saudi Arabia)

  • Mario Casillo

    (University of Salerno, Italy)

  • Francesco Colace

    (University of Salerno, Italy)

  • Dajana Conte

    (University of Salerno, Italy)

  • Marco Lombardi

    (University of Salerno, Italy)

  • Domenico Santaniello

    (University of Salerno, Italy)

  • Carmine Valentino

    (University of Salerno, Italy)

Abstract

In today's society, the continuous exchange of vast amounts of information, often irrelevant or misleading, highlights the need for greater awareness to distinguish between accurate and false information. Recognizing the reliability of information is critical to limiting the spread of fake news, a pervasive problem affecting various sectors, influencing public opinion, and shaping decisions in health care, politics, culture, and history. This paper proposes a methodology to assess the veracity of information, leveraging natural language processing (NLP) and probabilistic models to extract relevant features and predict the reliability of content. The features analyzed include semantic, syntactic, and social dimensions. The proposed methodology was tested using datasets that include social media news and comments captured during the lockdown due to COVID-19, providing relevant context for the analysis. Experimental validation of these different datasets yields promising results, demonstrating the effectiveness of the proposed approach.

Suggested Citation

  • Varsha Arya & Razaz Waheeb Attar & Ahmed Alhomoud & Mario Casillo & Francesco Colace & Dajana Conte & Marco Lombardi & Domenico Santaniello & Carmine Valentino, 2024. "FANE: A FAke NEws Detector Based on Syntactic, Semantic, and Social Features Bayesian Analysis," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-21, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-21
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