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 COVID-19 lockdown, providing relevant context for the analysis. Experimental validation of these different datasets yields promising results, demonstrating the effectiveness of the proposed approach.
FANE: A FAke NEws Detector Based on Syntactic, Semantic, and Social Features Bayesian Analysis
Mario Casillo;Francesco Colace;Dajana Conte;Marco Lombardi;Domenico Santaniello;Carmine Valentino
In corso di stampa
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 COVID-19 lockdown, providing relevant context for the analysis. Experimental validation of these different datasets yields promising results, demonstrating the effectiveness of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
