{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:31:39Z","timestamp":1771914699479,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701142, 71971170,  and 71971067"],"award-info":[{"award-number":["71701142, 71971170,  and 71971067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M640346"],"award-info":[{"award-number":["2018M640346"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Philosophy and Social Science Planning Project","award":["TJKS19XSX-015"],"award-info":[{"award-number":["TJKS19XSX-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Adverse drug reactions (ADRs) are a huge public health issue. Identifying text that mentions ADRs from a large volume of social media data is important. However, we need to address two challenges for high-performing ADR-related text detection: the data imbalance problem and the requirement of simultaneously using data-driven information and handcrafted information. Therefore, we propose an approach named multi-view active learning using domain-specific and data-driven document representations (MVAL4D), endeavoring to enhance the predictive capability and alleviate the requirement of labeled data. Specifically, a new view-generation mechanism is proposed to generate multiple views by simultaneously exploiting various document representations obtained using handcrafted feature engineering and by performing deep learning methods. Moreover, different from previous active learning studies in which all instances are chosen using the same selection criterion, MVAL4D adopts different criteria (i.e., confidence and informativeness) to select potentially positive instances and potentially negative instances for manual annotation. The experimental results verify the effectiveness of MVAL4D. The proposed approach can be generalized to many other text classification tasks. Moreover, it can offer a solid foundation for the ADR mention extraction task, and improve the feasibility of monitoring drug safety using social media data.<\/jats:p>","DOI":"10.3390\/info13040189","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T12:11:14Z","timestamp":1649419874000},"page":"189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying Adverse Drug Reaction-Related Text from Social Media: A Multi-View Active Learning Approach with Various Document Representations"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"},{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}]},{"given":"Lihua","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"}]},{"given":"Chenghong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"}]},{"given":"Songzheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Management, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jbi.2015.02.004","article-title":"Utilizing social media data for pharmacovigilance: A review","volume":"54","author":"Sarker","year":"2015","journal-title":"J. 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