article

Finding news-topic oriented influential twitter users based on topic related hashtag community detection

Authors:

Takehiro TokudaAuthors Info & Claims

Journal of Web Engineering, Volume 13, Issue 5-6

Pages 405 - 429

Published: 01 November 2014 Publication History

Abstract

Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user's retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations.

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Cited By

Panchendrarajan RSaxena A(2022)Topic-based influential user detection: a surveyApplied Intelligence10.1007/s10489-022-03831-753:5(5998-6024)Online publication date: 5-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03831-7
Bo HMcConville RHong JLiu W(2020)Social Network Influence Ranking via Embedding Network Interactions for User RecommendationCompanion Proceedings of the Web Conference 202010.1145/3366424.3383299(379-384)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3383299
Zhao ZZhou HZhang BJi FLi C(2019)Identifying High Influential Users in Social Media by Analyzing Users’ BehaviorsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18251236:6(6207-6218)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-182512
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Finding news-topic oriented influential twitter users based on topic related hashtag community detection
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Published In

cover image Journal of Web Engineering

Journal of Web Engineering Volume 13, Issue 5-6

November 2014

178 pages

ISSN:1540-9589

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Publisher

Rinton Press, Incorporated

Paramus, NJ

Publication History

Published: 01 November 2014

Revised: 20 May 2014

Received: 02 July 2013

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Cited By

Panchendrarajan RSaxena A(2022)Topic-based influential user detection: a surveyApplied Intelligence10.1007/s10489-022-03831-753:5(5998-6024)Online publication date: 5-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03831-7
Bo HMcConville RHong JLiu W(2020)Social Network Influence Ranking via Embedding Network Interactions for User RecommendationCompanion Proceedings of the Web Conference 202010.1145/3366424.3383299(379-384)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3383299
Zhao ZZhou HZhang BJi FLi C(2019)Identifying High Influential Users in Social Media by Analyzing Users’ BehaviorsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18251236:6(6207-6218)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-182512
Hamzehei AWong RKoutra DChen F(2019)Collaborative topic regression for predicting topic-based social influenceMachine Language10.1007/s10994-018-05776-w108:10(1831-1850)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10994-018-05776-w
Tidke BMehta RDhanani J(2019)Consensus-based aggregation for identification and ranking of top-k influential nodesNeural Computing and Applications10.1007/s00521-019-04568-032:14(10275-10301)Online publication date: 6-Nov-2019
https://dl.acm.org/doi/10.1007/s00521-019-04568-0
Noro TTokuda T(2016)Searching for relevant tweets based on topic-related user activitiesJournal of Web Engineering10.5555/3177210.317721415:3-4(249-276)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.5555/3177210.3177214

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