research-article

A methodology for community detection in Twitter

Authors:

Ádamo Santana,

Márcia PinheiroAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 1006 - 1009

https://doi.org/10.1145/3106426.3117760

Published: 23 August 2017 Publication History

Abstract

The microblogging service Twitter is one of the world's most popular online social networks and assembles a huge amount of data produced by interactions between users. A careful analysis of this data allows identifying groups of users who share similar traits, opinions, and preferences. We call community detection the process of user group identification, which grants valuable insights not available upfront. In order to extract useful knowledge from Twitter data many methodologies have been proposed, which define the attributes to be used in community detection problems by manual and empirical criteria - oftentimes guided by the aimed type of community and what the researcher attaches importance to. However, such approach cannot be generalized because it is well known that the task of finding out an appropriate set of attributes leans on context, domain, and data set. Aiming to the advance of community detection domain, reduce computational cost and improve the quality of related researches, this paper proposes a standard methodology for community detection in Twitter using feature selection methods. Results of the present research directly affect the way community detection methodologies have been applied to Twitter and quality of outcomes produced.

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Cited By

Grande-Ramírez JAguilar-Lasserre AArrioja-Carrera GDomínguez-Herrera J(2024)Applications and Resources for Social Media Sentiment Analysis: A Strategic Planning Case StudyManagement Engineering in Emerging Economies10.1007/978-3-031-54485-9_6(121-156)Online publication date: 9-May-2024
https://doi.org/10.1007/978-3-031-54485-9_6
Menezes PJustino ABarata BJacob Junior ALobato F(2023)Market Overview in Social CRM: An Analysis of Job Advertisements2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00092(555-562)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00092
Francisco MCastro J(2023)A Methodology to Quickly Perform Opinion Mining and Build Supervised Datasets Using Social Networks MechanicsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325082235:9(9797-9808)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250822
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A methodology for community detection in Twitter
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 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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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Funding Sources

Amazon Foundation for Studies and Research (Fapespa)
German Academic Exchange Service (DAAD)
National Council for the Improvement of Higher Education (CAPES)
National Council for Scientific and Technological Development (CNPq)

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WI '17

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SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

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https://doi.org/10.1007/978-3-031-54485-9_6
Menezes PJustino ABarata BJacob Junior ALobato F(2023)Market Overview in Social CRM: An Analysis of Job Advertisements2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00092(555-562)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00092
Francisco MCastro J(2023)A Methodology to Quickly Perform Opinion Mining and Build Supervised Datasets Using Social Networks MechanicsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325082235:9(9797-9808)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250822
Pricopi ACunningham P(2023)Social Network Analysis of Irish Covid-19 Interactions on Telegram2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)10.1109/AICS60730.2023.10470867(1-8)Online publication date: 7-Dec-2023
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Mazza MCola GTesconi M(2023)Modularity-based approach for tracking communities in dynamic social networksKnowledge-Based Systems10.1016/j.knosys.2023.111067281:COnline publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111067
Kanavos AVoutos YGrivokostopoulou FMylonas P(2022)Evaluating Methods for Efficient Community Detection in Social NetworksInformation10.3390/info1305020913:5(209)Online publication date: 19-Apr-2022
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Grande-Ramírez JRoldán-Reyes EAguilar-Lasserre AJuárez-Martínez U(2022)Integration of Sentiment Analysis of Social Media in the Strategic Planning Process to Generate the Balanced ScorecardApplied Sciences10.3390/app12231230712:23(12307)Online publication date: 1-Dec-2022
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Thakur N(2022)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research QuestionsAnalytics10.3390/analytics10200071:2(72-97)Online publication date: 23-Sep-2022
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Jastania ZAbbasi RAslam MKhanzada TGhori K(2022)Analyzing Public Discussions About #SaudiWomenCanDrive Using Network ScienceIEEE Access10.1109/ACCESS.2021.314007310(4739-4749)Online publication date: 2022
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Dritsas ETrigka MVonitsanos GKanavos AMylonas P(2021)Aspect-Based Community Detection of Cultural Heritage Streaming Data2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA52424.2021.9555549(1-4)Online publication date: 12-Jul-2021
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