An image and text-based multimodal model for detecting fake news in OSN's.

Digital Mass Media has become the new paradigm of communication that revolves around online social networks. The increase in the utilization of online social networks (OSNs) as the primary source of information and the increase of online social platforms providing such news has increased the scope of spreading fake news. People spread fake news in multimedia formats like images, audio, and video. Visual-based news is prone to have a psychological impact on the users and is often misleading. Therefore, Multimodal frameworks for detecting fake posts have gained demand in recent times. This paper proposes a framework that flags fake posts with Visual data embedded with text. The proposed framework works on data derived from the Fakeddit dataset, with over 1 million samples containing text, image, metadata, and comments data gathered from a wide range of sources, and tries to exploit the unique features of fake and legitimate images. The proposed framework has different architectures to learn visual and linguistic models from the post individually. Image polarity datasets, derived from Flickr, are also considered for analysis, and the features extracted from these visual and text-based data helped in flagging news. The proposed fusion model has achieved an overall accuracy of 91.94%, Precision of 93.43%, Recall of 93.07%, and F1-score of 93%. The experimental results show that the proposed Multimodality model with Image and Text achieves better results than other state-of-art models working on a similar dataset.