Optimizing fake news detection for Arabic context: A multitask learning approach with transformers and an enhanced Nutcracker Optimization Algorithm

The rapid proliferation of news and posts across social media platforms has spawned a concerning wave of misinformation. Disseminating false information and news significantly threatens public health and safety. To address this critical issue, we present an innovative disinformation detection framework, leveraging the power of multi-task learning (MTL) and meta-heuristic techniques. Our framework harnesses the potential of an MTL approach and a state-of-the-art pre-trained Transformer-based model, enabling the extraction of comprehensive contextual features from Arabic social media posts. These contextual are input to an advanced feature selection (FS) model utilizing a modified Nutcracker Optimization Algorithm. Through extensive evaluation of diverse datasets of Arabic social media posts, our proposed framework achieves remarkable results. Notably, our framework attains an accuracy rate of 87% and 69% for binary and multi-classification, respectively. In addition, the developed method outperforms all compared algorithms. Our findings demonstrate the potency of our disinformation detection framework, serving as a robust tool in the battle against misinformation spread. By shedding light on the truth amidst the vast social media content, we can safeguard public health and empower individuals with reliable information.