A motif-based approach for identifying controversy

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Coletto, Mauro
dc.contributor.author Garimella, Kiran
dc.contributor.author Gionis, Aristides
dc.contributor.author Lucchese, Claudio
dc.date.accessioned 2018-09-06T10:17:16Z
dc.date.available 2018-09-06T10:17:16Z
dc.date.issued 2017
dc.identifier.citation Coletto , M , Garimella , K , Gionis , A & Lucchese , C 2017 , A motif-based approach for identifying controversy . in Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017 . AAAI PRESS , pp. 496-499 , International Conference on Web and Social Media , Montreal , Canada , 15/05/2017 . en
dc.identifier.isbn 9781577357889
dc.identifier.other PURE UUID: c0361592-36f2-4b27-b558-645d05ca72a9
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a-motifbased-approach-for-identifying-controversy(c0361592-36f2-4b27-b558-645d05ca72a9).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85029450855&partnerID=8YFLogxK
dc.identifier.other PURE LINK: https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15653/14838
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/26625718/motifs_controversy.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33873
dc.description | openaire: EC/H2020/654024/EU//SoBigData
dc.description.abstract Among the topics discussed in Social Media, some lead to controversy. A number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, and of investigating the global network structure. In this work we show that it is possible to detect controversy in social media by exploiting network motifs, i.e., local patterns of user interaction. The proposed approach allows for a language-independent and fine-grained and efficientto- compute analysis of user discussions and their evolution over time. The supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features. en
dc.format.extent 4
dc.format.extent 496-499
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/654024/EU//SoBigData
dc.relation.ispartof International Conference on Web and Social Media en
dc.relation.ispartofseries Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017 en
dc.rights openAccess en
dc.subject.other Computer Networks and Communications en
dc.subject.other 113 Computer and information sciences en
dc.title A motif-based approach for identifying controversy en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.subject.keyword Computer Networks and Communications
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201809064984
dc.type.version acceptedVersion


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