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Web"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>The sudden onset of the recently concluded COVID-19 pandemic has driven substantial progress in various scientific fields. One notable example is the comprehension of public vaccination attitudes and the timely monitoring of their fluctuations through social media platforms. This approach can serve as a cost-effective means to supplement surveys in gathering public vaccine hesitancy levels. In this article, we propose a deep learning framework leveraging textual posts on social media to extract and track users\u2019 vaccination stances in near real time. Compared to previous works, we integrate into the framework the recent posts of a user\u2019s social network friends to collaboratively detect the user\u2019s genuine attitude towards vaccination. Based on our annotated dataset from X (formerly known as Twitter), the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to the state-of-the-art text-only models. Using this framework, we successfully confirm the feasibility of using social media to track the evolution of vaccination attitudes in real life. In addition, we illustrate the generality of our framework in extracting other public opinions such as political ideology. We further show one practical use of our framework by validating the possibility of forecasting a user\u2019s vaccine hesitancy changes with information perceived from social media.<\/jats:p>","DOI":"10.1145\/3702654","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:59:18Z","timestamp":1733309958000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["\u201cDouble vaccinated, 5G boosted!\u201d: Learning Attitudes towards COVID-19 Vaccination from Social Media"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1415-4583","authenticated-orcid":false,"given":"Ninghan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8131-5092","authenticated-orcid":false,"given":"Xihui","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Sciencesadf, St. P\u00f6lten University of Applied Sciences, St. P\u00f6lten, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1226-5597","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhong","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, Aarhus Universitet, Aarhus, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-4112","authenticated-orcid":false,"given":"Jun","family":"Pang","sequence":"additional","affiliation":[{"name":"Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]}],"member":"320","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","first-page":"Article 3, 52","article-title":"A survey of figurative language and its computational detection in online social networks","volume":"14","author":"Abulaish Muhammad","year":"2020","unstructured":"Muhammad Abulaish, Ashraf Kamal, and Mohammed J. 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