Combating Misinformation: Insights into Datasets, Models and Evaluation Strategies for Fake News

Information integrity and public trust are seriously threatened in the digital age by the spread of false information and fake news. In order to counteract misinformation, the work explores important insights into the models, databases, and assessment techniques. First, the paper focuses on unexplored directions and limitations available in current literature to improve the resilience of false news detection systems. Using essential factors like dataset size, diversity, and the inclusion of contextually relevant features, the paper further examine the various datasets used for training and testing misinformation detection algorithms. Subsequently, examine the diverse machine learning models and methodologies, such as ensemble approaches, supervised learning, and deep learning, and study how well they work to recognize and categorize false information. Following that, performance metrics, and robustness against adversarial attacks are discussed as disinformation detection systems' evaluation techniques. Finally, a special focus is placed on the function of models produced through various techniques in improving accuracy. The study attempts to give practitioners and researchers with useful insights for creating more dependable and scalable misinformation mitigation systems by combining current techniques and highlighting knowledge gaps.