Using Artificial Intelligence Against the Phenomenon of Fake News: A Systematic Literature Review

Social networks like Facebook and Twitter have become an important way for people to connect and share their thoughts. The most important feature of social networks is the rapid sharing of information. In this context, users often share fake news without even knowing it. Fake news affects people's daily lives and its consequences can range from mere disturbing to misleading societies or even countries. The aim of this study was to provide a literature review that investigates how artificial intelligence tools are used in detecting fake news on social media and how successful they are in different fields. The study was developed using the methodology presented by Keela (2007), which is a formal methodology in computer science. The results of the study show that artificial intelligence tools such as machine learning and deep learning are widely used to develop systems for detecting fake news in various fields such as politics, sports, business, etc. and that these two tools have proven to be effective in classifying fake news. This study is intended to guide researchers as well as people involved in this field. It is believed that this study will help fill a gap in this field by presenting the main tools used for this purpose and shed light on further research. It is also hoped that this study will be a guide for researchers and individuals interested in the detection of fake news.