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Multi-Modal Fact Verification. please visit this link for details.
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 multimodal or cross-modal fact-verification. This workshop will encourage researchers from interdisciplinary domains working on multimodality 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.
Link to previos year workshop : Defactify @ AAAI 2022
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 multimodal tasks in general.
Social media for news consumption is double edged sword. On the one hand, its low cost, easy access and rapid circulation of information lead people to consume news from social media. On the other hand, it enables the wide spread of fake news, i.e., low quality news with the false information. It affects everyone including government, media, individual, health, law and order, and economy. Therefore, fake news detection on social media has recently become an appealing research topic. We encourage solution to fake news like automated fact checking at scale, early detction of fake news etc.
Hate speech is defined as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease. We encourage works that help in detection of Multi-Modal Hate-Speech.
It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. This workshop covers (but not limited to) the following topics: --
Paper Submission Link : https://easychair.org/conferences/?conf=defactify23
Multi-Modal Fact Verification. please visit this link for details.
Multi-Modal Fact Verification. please visit this link for details.
Dr. Amitava Das is a Core Faculty & Research Associate Professor of the Artificial Intelligence Institute, at the University of South Carolina, and an Advisory Scientist to Wipro AI.
Research interests : Code-Mixing
and Social Computing.
Organizing Activities [selective] :
• Memotion @SemEval2020 • SentiMix @SemEval2020 • Computational Approaches to Linguistic Code-Switching @LREC 2020 • CONSTRAINT @AAAI2021
Srijan Kumar is an assistant professor of CSE at Georgia Tech.
Research interests : Multi-X Misinformation and Malicious Actors: Multi-Platform, Multi-Modal, and Multi-Lingual
Manoj Chinnakotla is a principal
applied scientist of AI research at Microsoft.
Research interests : NLP, Information Retrieval, Machine Learning.
Dr. Amit Sheth is the founding Director of the Artificial Intelligence
Institute, and a CSE Professor at
University of South Carolina.
Research interests : Knowledge
Graph, NLP, Analysing Social
Media
Organizing Activities [selective] :
• Cysoc2021 @ ICWSM2021 • Emoji2021 @ICWSM2021 • KiLKGC 2021 @KGC21
Dr. Asif Ekbal is an Associate Professor of CSE at IIT Patna, India.
Research interests : NLP, CodeMixing and Social Computing.
Organizing Activities [selective] : • CONSTRAINT @AAAI2021
Masters student at University of California Los Angeles (UCLA).
Research interests : Natural Language Processing, Machine Learning, Social Computing, and Computer Vision.