{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:08Z","timestamp":1777854488593,"version":"3.51.4"},"reference-count":43,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T00:00:00Z","timestamp":1550707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["NW2018004"],"award-info":[{"award-number":["NW2018004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71373123"],"award-info":[{"award-number":["71373123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p>It is a fact that most of the rumours related to hot events or emergencies can be propagated rapidly on the hotbed of online social networks. In order to track the standpoints of the participants of rumour topics to regulate the development of rumour, we propose a multi-features model combining classifiers to classify the rumour standpoints, defined as classifying the standpoints of online social network conversations into one of \u2018agree\u2019, \u2018disagree\u2019, \u2018comment\u2019 or \u2018query\u2019 on previous comment about the rumour. Testing the performance of the combinatorial model \u2013 decision tree with adaptive boosting classifier and extremely randomised trees with adaptive boosting classifier \u2013 on different features, that is, structuring the weight matrix based on combination of term frequency (TF), inverse document frequency (IDF) and term frequency \u2013 inverse document frequency (TFIDF) method and constructing the features vector with Word2vec method. The experiments show that the combinatorial classifiers that exploit different combination features in the online social network conversations outperform binary classification; especially, the topology of the social network has a highly positive impact on the classification results. Furthermore, the \u2018comment\u2019 and \u2018query\u2019 of rumour standpoints have a better classification effect based on the features of different categories.<\/jats:p>","DOI":"10.1177\/0165551519828619","type":"journal-article","created":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T05:50:59Z","timestamp":1550728259000},"page":"191-204","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["The classification of rumour standpoints in online social network based on combinatorial classifiers"],"prefix":"10.1177","volume":"46","author":[{"given":"Jing","family":"Ma","sequence":"first","affiliation":[{"name":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2148-4337","authenticated-orcid":false,"given":"Yongcong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"bibr1-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2013.09.057"},{"key":"bibr2-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.09.055"},{"key":"bibr3-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0065331"},{"key":"bibr4-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2014.2313024"},{"key":"bibr5-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2339799"},{"key":"bibr6-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0150989"},{"key":"bibr7-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2012.02.004"},{"key":"bibr8-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/S0959-8049(02)00764-5"},{"key":"bibr9-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2011.05.008"},{"key":"bibr10-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.02.031"},{"key":"bibr11-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2017.04.162"},{"key":"bibr12-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1142\/S012918311850078X"},{"key":"bibr13-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1145\/3161603"},{"key":"bibr14-0165551519828619","first-page":"1589","volume-title":"Proceedings of the conference on empirical methods in natural language processing","author":"Qazvinian V"},{"key":"bibr15-0165551519828619","first-page":"3","volume-title":"Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT","author":"Hamidian S"},{"key":"bibr16-0165551519828619","first-page":"747","volume-title":"Proceedings of the international AAAI conference on web and social media","author":"Zeng L"},{"key":"bibr17-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.10.014"},{"key":"bibr18-0165551519828619","first-page":"71","volume-title":"Proceedings first workshop on social media analytics of the","author":"Mendoza M"},{"key":"bibr19-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806651"},{"key":"bibr20-0165551519828619","first-page":"1034","volume-title":"Proceedings of the 2013 IEEE\/ACM international conference on advances in social networks analysis and mining","author":"Oostdijk N"},{"key":"bibr21-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23989"},{"key":"bibr22-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12011"},{"key":"bibr23-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(99)00114-4"},{"key":"bibr24-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.E97.D.1677"},{"key":"bibr25-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.10.010"},{"key":"bibr26-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2698463"},{"key":"bibr27-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2382600"},{"key":"bibr28-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23332"},{"key":"bibr29-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2345379"},{"key":"bibr30-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.011"},{"key":"bibr31-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.11.004"},{"key":"bibr32-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1108\/00220410410560573"},{"key":"bibr33-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1108\/00220410410560582"},{"key":"bibr34-0165551519828619","unstructured":"Zubiaga A, Liakata M, Procter R, et al. PHEME rumour scheme dataset: journalism use case, 2016, https:\/\/figshare.com\/articles\/PHEME_rumour_scheme_dataset_journalism_use_case\/2068650 (accessed 5 February 2019)."},{"key":"bibr35-0165551519828619","doi-asserted-by":"crossref","unstructured":"Zubiaga A, Liakata M, Procter R. Crowdsourcing the annotation of rumourous conversations in social media. In: Proceedings of the 24th international conference on World Wide Web (WWW), Florence, 18\u201322 May 2015, pp.347\u2013353. New York: ACM.","DOI":"10.1145\/2740908.2743052"},{"key":"bibr36-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450342480"},{"key":"bibr37-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-017-2574-9"},{"key":"bibr38-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"bibr39-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-050058-4.50007-3"},{"key":"bibr40-0165551519828619","volume-title":"Classification and regression trees","author":"Breiman L","year":"1984"},{"key":"bibr41-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"bibr42-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"bibr43-0165551519828619","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519828619","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0165551519828619","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519828619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T23:08:52Z","timestamp":1777504132000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0165551519828619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,21]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["10.1177\/0165551519828619"],"URL":"https:\/\/doi.org\/10.1177\/0165551519828619","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,21]]}}}