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Inf. Syst."],"published-print":{"date-parts":[[2019,4,30]]},"abstract":"<jats:p>Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a conversation around a rumour as either supporting, denying or questioning the rumour. Using a Gaussian Process classifier, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will show both ordinary users of Twitter and professional news practitioners how others orient to the disputed veracity of a rumour, with the final aim of establishing its actual truth value.<\/jats:p>","DOI":"10.1145\/3295823","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T19:36:17Z","timestamp":1550172977000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Gaussian Processes for Rumour Stance Classification in Social Media"],"prefix":"10.1145","volume":"37","author":[{"given":"Michal","family":"Lukasik","sequence":"first","affiliation":[{"name":"University of Sheffield, Sheffield, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kalina","family":"Bontcheva","sequence":"additional","affiliation":[{"name":"University of Sheffield, Sheffield, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor","family":"Cohn","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arkaitz","family":"Zubiaga","sequence":"additional","affiliation":[{"name":"University of Warwick, Coventry, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Liakata","sequence":"additional","affiliation":[{"name":"University of Warwick and Alan Turing Institute, Coventry, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"Procter","sequence":"additional","affiliation":[{"name":"University of Warwick and Alan Turing Institute, Coventry, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2021109.2021114"},{"key":"e_1_2_2_2_1","unstructured":"G. W. Allport and L. Postman. 1947. The psychology of rumor. J. Clin. Psychol. (1947). https:\/\/psycnet.apa.org\/record\/1948-00288-000.  G. W. Allport and L. Postman. 1947. The psychology of rumor. J. Clin. Psychol. (1947). https:\/\/psycnet.apa.org\/record\/1948-00288-000."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000036"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1190"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1108\/IntR-05-2012-0095"},{"key":"e_1_2_2_6_1","volume-title":"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL\u201913)","author":"Cohn Trevor","year":"2013","unstructured":"Trevor Cohn and Lucia Specia . 2013 . Modelling annotator bias with multi-task Gaussian processes: An application to machine translation quality estimation . In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL\u201913) . 32--42. Trevor Cohn and Lucia Specia. 2013. Modelling annotator bias with multi-task Gaussian processes: An application to machine translation quality estimation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL\u201913). 32--42."},{"key":"e_1_2_2_7_1","volume-title":"European Semantic Web Conference ESWC. 25--29","author":"Derczynski Leon","year":"2015","unstructured":"Leon Derczynski , Kalina Bontcheva , Michal Lukasik , Thierry Declerck , Arno Scharl , Georgi Georgiev , Petya Osenova , Toms Pariente Lobo , Anna Kolliakou , Robert Stewart , Sara-Jayne Terp , Geraldine Wong , Christian Burger , Arkaitz Zubiaga , Rob Procter , and Maria Liakata . 2015 . PHEME: Computing veracity the fourth challenge of big social data . In European Semantic Web Conference ESWC. 25--29 . 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Res. 36 , 1 (1969), 169 -- 171 . Tamotsu Shibutani. 1969. Improvised news: A sociological study of rumor. Soc. Res. 36, 1 (1969), 169--171.","journal-title":"Soc. Res."},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-2715"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3162956"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025892"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2893478"},{"key":"e_1_2_2_44_1","volume-title":"Proceedings of the 10th International AAAI Conference on Web and Social Media.","author":"Zeng Li","unstructured":"Li Zeng , Kate Starbird , and Emma S. Spiro . 2016. # unconfirmed: Classifying rumor stance in crisis-related social media messages . In Proceedings of the 10th International AAAI Conference on Web and Social Media. Li Zeng, Kate Starbird, and Emma S. Spiro. 2016. # unconfirmed: Classifying rumor stance in crisis-related social media messages. 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