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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic.<\/jats:p>","DOI":"10.1145\/3480246","type":"journal-article","created":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T04:28:40Z","timestamp":1634963320000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model"],"prefix":"10.1145","volume":"16","author":[{"given":"Yuanchun","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruicheng","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Sciences, The University of Southern Queensland, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianshan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yezheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-020-0884-z"},{"issue":"3","key":"e_1_3_3_3_2","article-title":"Inside out and outside in: How the COVID-19 pandemic affects self-disclosure on social media","volume":"55","author":"Nabity-Grover","year":"2020","unstructured":"Nabity-Grover, Teagen Cheung, Christy M. 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