Detection of Hidden Communities in Twitter Discussions of Varying Volumes

The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Stu...

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Autores principales:	Ivan Blekanov, Svetlana S. Bodrunova, Askar Akhmetov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	social networks user discussions user web-graph clustering hidden community detection Infomap Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/026f432622f8412f8e6357aaf6db0b61
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spelling	oai:doaj.org-article:026f432622f8412f8e6357aaf6db0b612021-11-25T17:40:04ZDetection of Hidden Communities in Twitter Discussions of Varying Volumes10.3390/fi131102951999-5903https://doaj.org/article/026f432622f8412f8e6357aaf6db0b612021-11-01T00:00:00Zhttps://www.mdpi.com/1999-5903/13/11/295https://doaj.org/toc/1999-5903The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies of online communities have clear social implications, as they allow for assessment of preference-based user grouping and the detection of socially hazardous groups. The aim of this study is to comparatively assess the algorithms that effectively analyze large user networks and extract hidden user communities from them. The results we have obtained show the most suitable algorithms for Twitter datasets of different volumes (dozen thousands, hundred thousands, and millions of tweets). We show that the Infomap and Leiden algorithms provide for the best results overall, and we advise testing a combination of these algorithms for detecting discursive communities based on user traits or views. We also show that the generalized <i>K</i>-means algorithm does not apply to big datasets, while a range of other algorithms tend to prioritize the detection of just one big community instead of many that would mirror the reality better. For isolating overlapping communities, the GANXiS algorithm should be used, while OSLOM is not advised.Ivan BlekanovSvetlana S. BodrunovaAskar AkhmetovMDPI AGarticlesocial networksuser discussionsuser web-graphclusteringhidden community detectionInfomapInformation technologyT58.5-58.64ENFuture Internet, Vol 13, Iss 295, p 295 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social networks user discussions user web-graph clustering hidden community detection Infomap Information technology T58.5-58.64
spellingShingle	social networks user discussions user web-graph clustering hidden community detection Infomap Information technology T58.5-58.64 Ivan Blekanov Svetlana S. Bodrunova Askar Akhmetov Detection of Hidden Communities in Twitter Discussions of Varying Volumes
description	The community-based structure of communication on social networking sites has long been a focus of scholarly attention. However, the problem of discovery and description of hidden communities, including defining the proper level of user aggregation, remains an important problem not yet resolved. Studies of online communities have clear social implications, as they allow for assessment of preference-based user grouping and the detection of socially hazardous groups. The aim of this study is to comparatively assess the algorithms that effectively analyze large user networks and extract hidden user communities from them. The results we have obtained show the most suitable algorithms for Twitter datasets of different volumes (dozen thousands, hundred thousands, and millions of tweets). We show that the Infomap and Leiden algorithms provide for the best results overall, and we advise testing a combination of these algorithms for detecting discursive communities based on user traits or views. We also show that the generalized <i>K</i>-means algorithm does not apply to big datasets, while a range of other algorithms tend to prioritize the detection of just one big community instead of many that would mirror the reality better. For isolating overlapping communities, the GANXiS algorithm should be used, while OSLOM is not advised.
format	article
author	Ivan Blekanov Svetlana S. Bodrunova Askar Akhmetov
author_facet	Ivan Blekanov Svetlana S. Bodrunova Askar Akhmetov
author_sort	Ivan Blekanov
title	Detection of Hidden Communities in Twitter Discussions of Varying Volumes
title_short	Detection of Hidden Communities in Twitter Discussions of Varying Volumes
title_full	Detection of Hidden Communities in Twitter Discussions of Varying Volumes
title_fullStr	Detection of Hidden Communities in Twitter Discussions of Varying Volumes
title_full_unstemmed	Detection of Hidden Communities in Twitter Discussions of Varying Volumes
title_sort	detection of hidden communities in twitter discussions of varying volumes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/026f432622f8412f8e6357aaf6db0b61
work_keys_str_mv	AT ivanblekanov detectionofhiddencommunitiesintwitterdiscussionsofvaryingvolumes AT svetlanasbodrunova detectionofhiddencommunitiesintwitterdiscussionsofvaryingvolumes AT askarakhmetov detectionofhiddencommunitiesintwitterdiscussionsofvaryingvolumes
_version_	1718412084793835520

Detection of Hidden Communities in Twitter Discussions of Varying Volumes

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