A Novel Hybrid Algorithm for Fake News Detection

The proliferation of erroneous information on social media has a deleterious effect on both people and society. In order to mitigate the drawbacks of social media, it is crucial to distinguish between authentic and misleading information. The proposed research presents a novel method to tackle the issue of identifying misinformation on social media. The main objective is to reframe the false news detection issue as an optimization problem and use two specialized metaheuristic algorithms, salp swarm optimization and grey wolf optimization, to address it. The proposed detection method is a three-step model wherein preprocessing the data is the fundamental step. The second step involves modifying grey wolf optimization and salp swarm optimization thereby creating a new false news detection model while the final step involves the testing of the proposed false news detection model. Three separate real-world datasets have been used for training the proposed false news detection model, conducting the data analysis, performing the statistical tests, benchmarking the proposed algorithms, and generating fruitful insights through reporting and visualization. The findings demonstrate that amongst the existing AI algorithms tested so far, the grey wolf optimization algorithm outperforms (accuracy = 0.97, precision = 0.97, recall = 1.0, f-score = 0.98) salp swarm optimization in addressing various social media issues.