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

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), and Carmine Valentino (University of Salerno, Italy)
Copyright: © 2024 | Pages: 21
DOI: 10.4018/IJSWIS.360785

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.
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Introduction

The advent of new methods of disseminating information has provided humanity with many advantages. Social media, in particular, has provided opportunities for people from different backgrounds and cultures to exchange views and compare perspectives on common interests. These extraordinary opportunities surpass interconnectedness, representing significant influence in various areas. The exponential amplification of traditional word of mouth through digital platforms enhances the ability to connect individuals worldwide. Moreover, social media is not limited to being a networking tool; it serves as an effective medium for communication. Every day, users access content and opinions that allow them to interact with a wealth of information. Reflecting on recent global experiences, particularly during the COVID-19 lockdown (Pennycook et al., 2020), people have relied heavily on virtual interactions, with social media becoming a primary channel for socialization and information exchange.

However, the significant benefits of social media and the broader internet landscape should be confronted with new and emerging challenges. The ease with which information can be disseminated to a wide audience and the possibility of blending into the masses and creating independent information channels have led to the misuse of these new media. Among the various problems that have emerged, fake news stands out as one of the most prominent concerns.

Fake news is a significant and particularly damaging subset of the broader phenomenon of disinformation. This term refers to deliberately disseminating false or misleading information to deceive or manipulate the public. While disinformation can take many forms, from half-truths to distorted interpretations of facts, fake news is intentionally fabricated to appear true, often presented as legitimate but designed to mislead readers about political, economic, health, or social issues. Misinformation can be intentional and unintentional: when misinformation or misleading information is unknowingly spread, it is called misinformation, an unintentional form. In contrast, fake news is a deliberate product created with the specific goal of misleading. They often appear as genuine news, complete with catchy headlines, fictitious or manipulated sources, and plausible contexts to legitimize the fake news.

Fake news can serve different purposes, sometimes causing “information disorder” phenomena (Wardle & Derakhshan, 2017) and becoming a significant social phenomenon (Baccarella et al., 2018). Notable examples include the role of fake news in the 2016 U.S. general election (Abd-Alrazaq et al., 2020) and in the U.K. Brexit referendum. Although the concept of fake news is intuitive and easy to understand, providing a definition that captures all its facets is more complex. The interdisciplinary nature of the topic makes it difficult to identify the core characteristics that define fake news in all relevant fields. As a result, numerous studies have attempted to define fake news (Zhou & Zafarani, 2020) and analyze it in depth (Gelfert, 2018; Tandoc et al., 2018). A critical feature of fake news is the intent to deceive readers (Yang et al., 2019) through false information. Specifically, Allcott and Gentzkow define fake news as intentionally and verifiably false news articles that likely mislead readers (Allcott & Gentzkow, 2017; Zhan, Z. et al., 2022).

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