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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.

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-Frequently studied structural property of networks is community structure which is described as a group of users. User interactions inside the group are more than those outside the group. Communities in networks may be overlapped as users belong to multiple groups at once. This paper proposes a new socially inspired metaheuristic search and optimization algorithm, Parliamentary Optimization Algorithm (POA), to acquire promising solutions to overlapping community detection problems considering multiple objectives. The salient and unique feature of this work is that for the first time POA has been designed as a multi-objective search method for overlapping community detection. There is not any work about multi-objective overlapping community detection problem in the related literature. For this reason, simulation results of the proposed algorithm have not been compared with any results of works. The experimental studies on both artificial and real world social networks indicate that the POA ensures beneficial results for defining multi-objective overlapping community structure. A novel and interesting application area of POA has been introduced with this work. Parallel and distributed versions of social based POA with optimized parameters may also be efficiently designed and used for different social network problems.