A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms

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.

CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT

“…A brief overview of notable relevant works to the proposed model is given in this section. According to the literature, a traditional Machine Learning (ML) [ 1 , 2 , 9 , 22 , 30 , 31 , 33 ] and Deep Learning (DL) [ 5 , 7 , 18 , 25 , 41 , 47 , 54 , 55 ] techniques effectively used textual, visual, and social-context features to solve the automated fake news detection problem. Furthermore, existing research is divided into two categories: unimodal and multimodal fake news identification.…”

“…Furthermore, existing research is divided into two categories: unimodal and multimodal fake news identification. To detect fake news, the former uses either one of the content-based [ 1 , 2 , 5 , 7 , 9 , 18 , 22 , 25 , 30 , 31 , 33 , 41 , 47 , 54 , 55 ] or social-context-based features [ 13 , 23 , 24 , 26 , 27 , 38 , 50 , 51 ], while the latter uses a combination of any single modality feature [ 20 , 43 , 44 , 49 , 53 ].…”

“…Recently, Guo et al [ 12 ] investigated and presented a comprehensive review on recent research challenges, novel techniques, and details of datasets in the field of fake information detection. Ozbay and Alatas [ 30 ] utilized a meta-heuristic technique, grey wolf optimizer (GWO) and salp swarm optimizer (SSO) for fake news identification. Perez-Rosas et al [ 33 ] developed two novel datasets for automatic fake news detection, which consists of seven various domains of news.…”

“…Unimodal content-based FND approaches are ineffective at detecting fake news because they use textual [ 1 , 2 , 5 , 7 , 9 , 18 , 22 , 25 , 30 , 31 , 33 , 41 , 47 , 54 , 55 ] and visual features [ 4 , 11 , 14 , 28 , 34 , 56 , 59 ] separately. In general, no underlying semantics were discovered among these features, resulting in poor classification.…”

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

“…Recently, metaheuristic methods have been employed to resolve complex problems such as scheduling [ 30 ] and detection problems [ 31 ]. The performance of metaheuristic algorithms in solving efficiently these problems makes them a good alternative to train neural networks with large parameters as they are simple to use, are independent form gradient, and avoid local optima [ 32 , 33 ].…”