Finding Topical Experts in Twitter via Query-dependent Personalized PageRank

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Lahoti, Preethi
dc.contributor.author De Francisci Morales, Gianmarco
dc.contributor.author Gionis, Aristides
dc.date.accessioned 2018-09-06T10:16:31Z
dc.date.available 2018-09-06T10:16:31Z
dc.date.issued 2017
dc.identifier.citation Lahoti , P , De Francisci Morales , G & Gionis , A 2017 , Finding Topical Experts in Twitter via Query-dependent Personalized PageRank . in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 . ACM , pp. 155-162 , IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining , Sydney , Australia , 31/07/2017 . DOI: 10.1145/3110025.3110044 en
dc.identifier.isbn 978-1-4503-4993-2
dc.identifier.other PURE UUID: 64b9812f-8664-4a95-af17-8430236e72b8
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/finding-topical-experts-in-twitter-via-querydependent-personalized-pagerank(64b9812f-8664-4a95-af17-8430236e72b8).html
dc.identifier.other PURE LINK: http://doi.acm.org/10.1145/3110025.3110044
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/26625803/twitter_experts.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33858
dc.description | openaire: EC/H2020/654024/EU//SoBigData
dc.description.abstract Finding topical experts on micro-blogging sites, such as Twitter, is an essential information-seeking task. In this paper, we introduce an expert-finding algorithm for Twitter, which can be generalized to find topical experts in any social network with endorsement features. Our approach combines traditional link analysis with text mining. It relies on crowd-sourced data from Twitter lists to build a labeled directed graph called the endorsement graph, which captures topical expertise as perceived by users. Given a text query, our algorithm uses a dynamic topic-sensitive weighting scheme, which sets the weights on the edges of the graph. Then, it uses an improved version of query-dependent PageRank to find important nodes in the graph, which correspond to topical experts. In addition, we address the scalability and performance issues posed by large social networks by pruning the input graph via a focused-crawling algorithm. Extensive evaluation on a number of different topics demonstrates that the proposed approach significantly improves on query-dependent PageRank, outperforms the current publicly-known state-of-the-art methods, and is competitive with Twitter's own search system, while using less than 0.05% of all Twitter accounts. en
dc.format.extent 8
dc.format.extent 155-162
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/654024/EU//SoBigData
dc.relation.ispartof IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining en
dc.relation.ispartofseries Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Finding Topical Experts in Twitter via Query-dependent Personalized PageRank en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Professorship Gionis A.
dc.contributor.department Qatar Computing Research Institute
dc.contributor.department Department of Computer Science
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201809064969
dc.identifier.doi 10.1145/3110025.3110044
dc.type.version acceptedVersion


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