Fake news detection algorithms – A systematic literature review

Abstract

Social media and news platforms make available to their users, in real-time and simultaneously, access to a significant amount of content that may be true or false. It is remarkable that, with the evolution of Industry 4.0 technologies, the production and dissemination of fake news also increased in recent years. Some content quickly reaches considerable popularity because it is accessed and shared on a large scale, especially in social networks, thus having a potential for going viral. Thus, this study aimed to identify the algorithms and software used for fake news detection. The choice for this combination is justified because in Brazil this process is carried out manually by verification agencies and thus, based on the mapping of the algorithms identified in the literature, an architecture proposal will be developed using artificial intelligence. As a methodology, a systematic literature review (SLR) was conducted in the Science Direct and Scopus databases using the keywords "fake news" and "machine learning" to locate reviews and research articles published in Engineering fields from 2018 to 2023. A total of 24 articles were analyzed, and the results pointed out that Facebook and X¹ were the social networks most used to disseminate fake news. Moreover, the main topics addressed were the COVID-19 pandemic and the United States presidential elections of 2016 and 2020. As for the most used algorithms, a predominance of neural networks was observed. The contribution of this study is in mapping the most used algorithms and their degree of assertiveness, as well as identifying the themes, countries and related researchers that help in the evolution of the fake news theme.