A Multi-Modal Framework for Fake News Analysis for Detection to Deconstruction

The research discusses a multimodal framework for analyzing and detecting fake news and understanding its impact on society. This framework employs diverse strategies, including linguistic analysis, social network monitoring, and visual assessment, to capture various aspects of fabricated information and its dissemination. The first component of the framework focuses on linguistic analysis, examining the language and textual content used in fake news articles to identify misleading information, biased language, and sensationalized headlines. The second component analyzes the role of social networks in spreading fake news, tracking its propagation through platforms like social media and identifying key actors and influencers involved in its dissemination. The third component involves visual assessment, examining the images and videos used in fake news to manipulate public perceptions and emotions, including the detection of doctored images and misleading illustrations.