Combating fake news is one of the burning societal crisis. It is difficult to expose false claims before they create a lot of damage. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. Research efforts and datasets on text fact verification could be found, but there is not much attention towards multi-modal or cross-modal fact-verification. This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos) fact-checking. At the same time, multimodal hate speech detection is an important problem but has not received much attention. Lastly, learning joint modalities his of interest to both Natural Language Processing (NLP) and Computer Vision (CV) forums.
Rationale: During the last decade, both the field of studies - NLP and CV have made significant progress due to the success strories of neural network. Mutimodal tasks like visual question-answering (VQA), image captioning, video captioning, caption based image retrieval, etc. started getting into the main spotlight either in NLP/CV forums. Mutimodality is the next big leap for the AI community. De-Factify is a specified forum to discuss on multimodal fake news, and hate speech related challenges. We also encourage discussion on multi-modal tasks in general.