research-article

iTop: interaction based topic centric community discovery on twitter

Authors:

Mayank PundirAuthors Info & Claims

PIKM '12: Proceedings of the 5th Ph.D. workshop on Information and knowledge

Pages 51 - 58

https://doi.org/10.1145/2389686.2389698

Published: 02 November 2012 Publication History

Abstract

Automatic detection of communities (or cohesive groups of actors in social network) in online social media platforms based on user interests and interaction is a problem that has recently attracted a lot of research attention. Mining user interactions on Twitter to discover such communities is a technically challenging information retrieval task. We present an algorithm - iTop - to discover interaction based topic centric communities by mining user interaction signals (such as @-messages and retweets) which indicate cohesion. iTop takes any topic as an input keyword and exploits local information to infer global topic-centric communities. We evaluate the discovered communities along three dimensions: graph based (node-edge quality), empirical-based (Twitter lists) and semantic based (frequent n-grams in tweets). We conduct experiments on a publicly available scrape of Twitter provided by InfoChimps via a web service. We perform a case study on two diverse topics - 'Computer Aided Design (CAD)' and 'Kashmir' to demonstrate the efficacy of iTop. Empirical results from both case studies show that iTop is successfully able to discover topic-centric, interaction based communities on Twitter.

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Cited By

Anwar M(2020)Query-Oriented Temporal Active Intimate Community SearchDatabases Theory and Applications10.1007/978-3-030-39469-1_17(206-215)Online publication date: 21-Jan-2020
https://doi.org/10.1007/978-3-030-39469-1_17
Das SAnwar M(2019)Discovering Topic-Oriented Highly Interactive Online CommunitiesFrontiers in Big Data10.3389/fdata.2019.000102Online publication date: 6-Jun-2019
https://doi.org/10.3389/fdata.2019.00010
Anwar MLiu CLi J(2019)Discovering and tracking query oriented active online social groups in dynamic information networkWorld Wide Web10.1007/s11280-018-0627-522:4(1819-1854)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11280-018-0627-5
Show More Cited By

Index Terms

iTop: interaction based topic centric community discovery on twitter
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

PIKM '12: Proceedings of the 5th Ph.D. workshop on Information and knowledge

November 2012

108 pages

ISBN:9781450317191

DOI:10.1145/2389686

Program Chairs:
Aparna S. Varde
Montclair State University
,
Fabian M. Suchanek
Max Planck Institute for Informatics

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2012

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November 2, 2012

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Overall Acceptance Rate 25 of 62 submissions, 40%

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Citations

Cited By

Anwar M(2020)Query-Oriented Temporal Active Intimate Community SearchDatabases Theory and Applications10.1007/978-3-030-39469-1_17(206-215)Online publication date: 21-Jan-2020
https://doi.org/10.1007/978-3-030-39469-1_17
Das SAnwar M(2019)Discovering Topic-Oriented Highly Interactive Online CommunitiesFrontiers in Big Data10.3389/fdata.2019.000102Online publication date: 6-Jun-2019
https://doi.org/10.3389/fdata.2019.00010
Anwar MLiu CLi J(2019)Discovering and tracking query oriented active online social groups in dynamic information networkWorld Wide Web10.1007/s11280-018-0627-522:4(1819-1854)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11280-018-0627-5
Sharma RMontesi DChampin PGandon FMédini LLalmas MIpeirotis P(2018)Investigating Similarity of Nodes' Attributes in Topological Based Communities.Companion Proceedings of the The Web Conference 201810.1145/3184558.3191564(1253-1260)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191564
Tian BLi W(2018)Community detection method based on mixed-norm sparse subspace clusteringNeurocomputing10.1016/j.neucom.2017.10.060275:C(2150-2161)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.10.060
Anwar MLiu CLi J(2018)Uncovering Attribute-Driven Active Intimate CommunitiesDatabases Theory and Applications10.1007/978-3-319-92013-9_9(109-122)Online publication date: 18-May-2018
https://doi.org/10.1007/978-3-319-92013-9_9
Lim KDatta A(2016)An interaction-based approach to detecting highly interactive Twitter communities using tweeting linksWeb Intelligence10.3233/WEB-16032814:1(1-15)Online publication date: 18-Feb-2016
https://doi.org/10.3233/WEB-160328
Sharma RMagnani MMontesi D(2015)Understanding community patterns in large attributed social networksProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2809330(1503-1508)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2809330
Wang JLiu ZZhao H(2015)Topic Oriented User Influence Analysis in Social NetworksProceedings of the 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Volume 0110.1109/WI-IAT.2015.202(123-126)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.1109/WI-IAT.2015.202
Guellil IBoukhalfa K(2015)Social big data mining: A survey focused on opinion mining and sentiments analysis2015 12th International Symposium on Programming and Systems (ISPS)10.1109/ISPS.2015.7244976(1-10)Online publication date: Apr-2015
https://doi.org/10.1109/ISPS.2015.7244976
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