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ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2023,4,14]]},"abstract":"<jats:p>While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. %\\ywi added \"have'' %\\ywFor RQ2, we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them.<\/jats:p>","DOI":"10.1145\/3579542","type":"journal-article","created":{"date-parts":[[2023,4,16]],"date-time":"2023-04-16T17:23:07Z","timestamp":1681665787000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4093-9552","authenticated-orcid":false,"given":"Yuping","family":"Wang","sequence":"first","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5082-7557","authenticated-orcid":false,"given":"Chen","family":"Ling","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6162-578X","authenticated-orcid":false,"given":"Gianluca","family":"Stringhini","sequence":"additional","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18036"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01452"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0165551515602847"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432948"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.763"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics. 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