Analysis of Natural Language Processing Algorithms to Detect False News in Social Networks. A Review of the Scientific Literature from 2015 - 2020 - Hernan Inga-Arteaga; Dayanne Mayta-Matienso; Michael Cabanillas-Carbonell (Communications of International Proceedings, 2020)
Modern society is characterized by the great diversity of information that is created and distributed at great speed thanks to technology. Due to this, artificial intelligence techniques are used in order to automate the work of classifying news for its veracity. The algorithms in the Natural Language Processing (NLP) family are especially used and studied with respect to this area. The tendency of information to be almost real time, sometimes prevents the correct analysis of sources, which can result in widespread misinformation by a news with false foundations, or half-truths. In this work 67 articles were systematized, from different databases (IEEE Xplore, Scopus, Science Direct, Scielo, Proquest). As a conclusion we can say that a large number of algorithms are useful for analyzing the veracity of news transmitted on social networks, also these can prevent the dissemination of false information, in addition to the algorithms in the family of natural language processing are especially useful for unstructured data environments such as social networks.
Keywords: Natural language processing; machine learning; social networks; fake news.