{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T19:37:27Z","timestamp":1775158647430,"version":"3.50.1"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62006093, 61803170"],"award-info":[{"award-number":["62006093, 61803170"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Qing Lan Project of Jiangsu Universities, Opening Foundation of Jiangsu Big Data Intelligent Engineering Laboratory of Soochow University","award":["SDGC2158"],"award-info":[{"award-number":["SDGC2158"]}]},{"name":"Project of Natural Science Research of Huai.an","award":["HABZ202119"],"award-info":[{"award-number":["HABZ202119"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>Stance detection aims to assign a stance label (in favor or against) to a post towards a specific target. In the literature, there are many studies focusing on this topic, and most of them treat stance detection as a supervised learning task. Therefore, a new classifier needs to be built from scratch on a well-prepared set of ground-truth data whenever predictions are needed for an unseen target. However, it is difficult to annotate the stance of a post, since a stance is a subjective attitude towards a target. Hence, it is necessary to learn the information from unlabeled data or other target data to help stance detection with a certain target. In this study, we propose a multi-target stance detection framework to integrate multi-target data together for stance detection. Since topic and sentiment are two important factors to identify the stance of a post in multi-target data, we propose an adversarial attention network to integrate multi-target data by detecting and connecting topic and sentiment information. In particular, the adversarial network is utilized to determine the topic and the sentiment of each post to collect some target-invariant information for stance detection. In addition, the attention mechanism is utilized to connect posts with a similar topic or sentiment to acquire some key information for stance detection. The experimental results not only demonstrate the effectiveness of the proposed model, but also indicate the importance of the topic and the sentiment information for stance detection using multi-target data.<\/jats:p>","DOI":"10.1145\/3544490","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T10:05:48Z","timestamp":1655373948000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Stance Detection with a Multi-Target Adversarial Attention Network"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2580-8957","authenticated-orcid":false,"given":"Qingying","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huaiyin Normal University, Huai\u2019an, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7018-0068","authenticated-orcid":false,"given":"Xuefeng","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Electronic &amp; Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6287-3021","authenticated-orcid":false,"given":"Jiajun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huaiyin Normal University, Huai\u2019an, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0839-9856","authenticated-orcid":false,"given":"Zhongqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Natural Language Processing Lab, Soochow University, Suzhou, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0905-7068","authenticated-orcid":false,"given":"Huiyan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huaiyin Normal University, Huai\u2019an, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","volume-title":"Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis","author":"Anand Pranav","year":"2011","unstructured":"Pranav Anand, Marilyn A. 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