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Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to users, and textual content is sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections between the modalities, e.g., text and image. Hence, many researchers have developed automatic techniques for detecting possible cross-modal discordance in web-based content. We analyze, categorize, and identify existing approaches in addition to the challenges and shortcomings they face to unearth new research opportunities in the field of multi-modal misinformation detection.<\/jats:p>","DOI":"10.1145\/3697349","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T11:16:06Z","timestamp":1728990966000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4603-7381","authenticated-orcid":false,"given":"Sara","family":"Abdali","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology College of Computing, Atlanta, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8346-8105","authenticated-orcid":false,"given":"Sina","family":"Shaham","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9994-9931","authenticated-orcid":false,"given":"Bhaskar","family":"Krishnamachari","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18036"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML\/PKDD\u201920)","author":"Abdali Sara","year":"2020","unstructured":"Sara Abdali, Neil Shah, and Evangelos E. 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