A Multi-feature Bayesian Approach for Fake News Detection

In a world flooded with information, often irrelevant, lucidity is power. Never as in this historical period can anyone, thanks to new technologies, participate as a protagonist in the debates raised about events and issues that affect our society. In this flood of information, remaining lucid and knowing how to discriminate between real and false becomes fundamental. In this scenario, a leading role is played by Fake News, information that is partly or entirely untrue, divulged through the Web, the media, or digital communication technologies. Fake news is characterized by an apparent plausibility, the latter fed by a distorted system of public opinion expectations, and by an amplification of the prejudices based on it, which facilitates its sharing and diffusion even in the absence of verification of the sources. Fake News is becoming a severe problem that affects various sectors of society: medicine, politics, culture, history are some of the areas that suffer most from the phenomenon of fake news, which can often generate significant social problems. This paper will introduce a probabilistic approach to determining the degree of truthfulness of the information. The system is based on the definition of some features, identified after an analysis of fake news in the literature through NLP-based approaches and statistical methods. The specified features will highlight the syntactic, semantic, and social features of the information. These features are combined in a Bayesian Network, previously trained on a dataset composed of fake news, to provide a probabilistic level of the truthfulness of the information analyzed. The proposed method has been tested in some real cases with very satisfactory results.