Shot Fake News Detection

Shot fake news detection focuses on automatically identifying false news articles using limited labeled data, a crucial challenge given the rapid spread of misinformation. Current research emphasizes few-shot and zero-shot learning approaches, leveraging large language models (LLMs) and incorporating multimodal features (text, images, etc.) to improve accuracy and efficiency. These methods often employ prompt engineering, meta-learning, and techniques to enhance knowledge transfer across domains, aiming to create robust and scalable fake news detection systems. The development of effective few-shot detection methods is vital for mitigating the harmful effects of fake news and advancing the field of automated fact-checking.