Fake News Detection Model

Fake news detection models aim to automatically identify false or misleading information online, a crucial task given the widespread impact of misinformation. Current research emphasizes multimodal approaches, leveraging text, images, and social context, often employing large vision-language models or ensembles of classifiers like those using majority voting techniques to improve accuracy and robustness. A key focus is enhancing model generalization and mitigating biases, including those stemming from entity prevalence or manipulative data poisoning, to ensure reliable performance across diverse domains and over time. These advancements are vital for improving information security and public discourse.