Fake news detection in online social networks using swarm intelligence based methods

In recent years, the rapid development of social media has changed the way people access information. Users can access information about social, economic, political and scientific events in the world through social media. The fact that the news on social media includes videos and pictures causes the loss of importance of traditional news platforms, such as television and newspapers. In addition, online social media provides advantages such as easy access to information, low cost and rapid dissemination of information. Although social media has many advantages, unfortunately, most of the news on social media may be changed by malicious people and therefore, it may not be reliable. Such news spreads quickly through social media and causes a negative impact on social media readers and users. Therefore, to reduce the negative effects of fake news, fake news on social media needs to be detected. Although fake news detection is a new area of research, it has attracted much attention. In this thesis, Fake News Detection problem, one of the most popular and interesting online social media problems, has been considered as an optimization problem. During the optimization study, four heuristic optimization algorithms have been used, two of them have been proposed for the first time in this thesis, which are the new adaptive optimization algorithms and thirty supervised artificial intelligence algorithms used in the literature and fake news detection results have been compared. Although the proposed method is very new, promising results have been achieved.