Abstract
Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.
Highlights
The development of online social media has changed the way people access information
The unstructured textual social media data can be considered as a search space and metaheuristic algorithm can be adapted as a search method for fake news detection (FND) problem
Researchers on text mining, social network analysis, and optimization can use and enhance the methods proposed in this study for solving different types of social media problems for to get more efficient results due to the promising results obtained from Grey Wolf Optimization (GWO)
Summary
The development of online social media has changed the way people access information. Due to many advantages of metaheuristic optimization algorithms, they have been efficiently used in solving many complex real-world problems. They are population-based global search methods, which do not start searching having a single candidate solution. As FND is one of the new complex real-world problems different more efficient methods need to be proposed for better performance with respect to different metrics. The metaheuristic algorithms may be efficiently used for solving these type of problems in order to obtain better performance than that of the existing approaches. The unstructured textual social media data can be considered as a search space and metaheuristic algorithm can be adapted as a search method for FND problem.
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