Finding topical experts in twitter via query-dependent personalized PageRank

Preethi Lahoti, Gianmarco Morales, Aristides Gionis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
PublisherAssociation for Computing Machinery, Inc
Pages155-162
Number of pages8
ISBN (Electronic)9781450349932
DOIs
Publication statusPublished - 31 Jul 2017
Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
Duration: 31 Jul 20173 Aug 2017

Other

Other9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
CountryAustralia
CitySydney
Period31/7/173/8/17

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Directed graphs
Scalability

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Lahoti, P., 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, ASONAM 2017 (pp. 155-162). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3110044

Finding topical experts in twitter via query-dependent personalized PageRank. / Lahoti, Preethi; Morales, Gianmarco; Gionis, Aristides.

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Association for Computing Machinery, Inc, 2017. p. 155-162.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lahoti, P, 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, ASONAM 2017. Association for Computing Machinery, Inc, pp. 155-162, 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Sydney, Australia, 31/7/17. https://doi.org/10.1145/3110025.3110044
Lahoti P, Morales G, Gionis A. 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, ASONAM 2017. Association for Computing Machinery, Inc. 2017. p. 155-162 https://doi.org/10.1145/3110025.3110044
Lahoti, Preethi ; Morales, Gianmarco ; Gionis, Aristides. / Finding topical experts in twitter via query-dependent personalized PageRank. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Association for Computing Machinery, Inc, 2017. pp. 155-162
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