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Data and Information Quality"],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre<jats:italic>ad hoc<\/jats:italic>pipeline to identify whether the information in a Twitter post (i.e., a \u201cTweet\u201d) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it)<jats:italic>before<\/jats:italic>the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking\u2014a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.<\/jats:p>","DOI":"10.1145\/3546914","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T11:03:00Z","timestamp":1660820580000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5361-0193","authenticated-orcid":false,"given":"Chaoyuan","family":"Zuo","sequence":"first","affiliation":[{"name":"Stony Brook University, Stony Brook, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0336-0258","authenticated-orcid":false,"given":"Ritwik","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Stony Brook University, Stony Brook, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5765-5102","authenticated-orcid":false,"given":"Fateme Hashemi","family":"Chaleshtori","sequence":"additional","affiliation":[{"name":"Colorado State University, Fort Collins, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2721-0628","authenticated-orcid":false,"given":"Hossein","family":"Shirazi","sequence":"additional","affiliation":[{"name":"Colorado State University, Fort Collins, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0714-7676","authenticated-orcid":false,"given":"Indrakshi","family":"Ray","sequence":"additional","affiliation":[{"name":"Colorado State University, Fort Collins, Colorado, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019600"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Firoj Alam Shaden Shaar Alex Nikolov Hamdy Mubarak Giovanni Da San Martino Ahmed Abdelali Fahim Dalvi Nadir Durrani Hassan Sajjad Kareem Darwish and Preslav Nakov. 2020. 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