Multimodal News

Multimodal news analysis focuses on understanding and leveraging the combined information from various data modalities (text, images, video) present in news articles to achieve tasks such as fake news detection, headline generation, and framing analysis. Current research emphasizes developing sophisticated models, often employing transformer architectures and pre-trained models like CLIP and BERT, to effectively fuse information across modalities and improve accuracy in downstream tasks. This field is significant because it addresses the challenges of analyzing increasingly complex and multimodal news sources, with applications ranging from combating misinformation to enhancing news summarization and understanding media bias.