A likelihood-based framework for the analysis of discussion threads

Online discussion threads are conversational cascades in the form of posted messages that can be generally found in social systems that comprise many-to-many interaction such as blogs, news aggregators or bulletin board systems. We propose a framework based on generative models of growing trees to a...

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Detalles Bibliográficos
Autores: Gómez, Vicenç, Kappen, Hilbert J., Litvak, Nelly, Kaltenbrunner, Andreas
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/26746
Acceso en línea:http://hdl.handle.net/10230/26746
http://dx.doi.org/10.1007/s11280-012-0162-8
Access Level:acceso abierto
Palabra clave:Discussion threads
Online conversations
Information cascades
Preferential attachment
Novelty
Maximum likelihood
Slashdot
Wikipedia
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spelling A likelihood-based framework for the analysis of discussion threadsGómez, VicençKappen, Hilbert J.Litvak, NellyKaltenbrunner, AndreasDiscussion threadsOnline conversationsInformation cascadesPreferential attachmentNoveltyMaximum likelihoodSlashdotWikipediaOnline discussion threads are conversational cascades in the form of posted messages that can be generally found in social systems that comprise many-to-many interaction such as blogs, news aggregators or bulletin board systems. We propose a framework based on generative models of growing trees to analyse the structure and evolution of discussion threads. We consider the growth of a discussion to be determined by an interplay between popularity, novelty and a trend (or bias) to reply to the thread originator. The relevance of these features is estimated using a full likelihood approach and allows to characterise the habits and communication patterns of a given platform and/or community. We apply the proposed framework on four popular websites: Slashdot, Barrapunto (a Spanish version of Slashdot), Meneame (a Spanish Digg-clone) and the article discussion pages of the English Wikipedia. Our results provide significant insight into understanding how discussion cascades grow and have potential applications in broader contexts such as community management or design of communication platforms.Springer201620162013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/26746http://dx.doi.org/10.1007/s11280-012-0162-8reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésWorld Wide Web. 2013;16(5):645-75.http://hdl.handle.net/10230/26270© The Author(s) 2012. This article is published with open access at Springerlink.com. his article is distributed under the terms of the Creative Commons Attribution/nLicense which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are creditedhttp://creativecommons.org/licenses/by/3.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/267462026-05-29T05:05:01Z
dc.title.none.fl_str_mv A likelihood-based framework for the analysis of discussion threads
title A likelihood-based framework for the analysis of discussion threads
spellingShingle A likelihood-based framework for the analysis of discussion threads
Gómez, Vicenç
Discussion threads
Online conversations
Information cascades
Preferential attachment
Novelty
Maximum likelihood
Slashdot
Wikipedia
title_short A likelihood-based framework for the analysis of discussion threads
title_full A likelihood-based framework for the analysis of discussion threads
title_fullStr A likelihood-based framework for the analysis of discussion threads
title_full_unstemmed A likelihood-based framework for the analysis of discussion threads
title_sort A likelihood-based framework for the analysis of discussion threads
dc.creator.none.fl_str_mv Gómez, Vicenç
Kappen, Hilbert J.
Litvak, Nelly
Kaltenbrunner, Andreas
author Gómez, Vicenç
author_facet Gómez, Vicenç
Kappen, Hilbert J.
Litvak, Nelly
Kaltenbrunner, Andreas
author_role author
author2 Kappen, Hilbert J.
Litvak, Nelly
Kaltenbrunner, Andreas
author2_role author
author
author
dc.subject.none.fl_str_mv Discussion threads
Online conversations
Information cascades
Preferential attachment
Novelty
Maximum likelihood
Slashdot
Wikipedia
topic Discussion threads
Online conversations
Information cascades
Preferential attachment
Novelty
Maximum likelihood
Slashdot
Wikipedia
description Online discussion threads are conversational cascades in the form of posted messages that can be generally found in social systems that comprise many-to-many interaction such as blogs, news aggregators or bulletin board systems. We propose a framework based on generative models of growing trees to analyse the structure and evolution of discussion threads. We consider the growth of a discussion to be determined by an interplay between popularity, novelty and a trend (or bias) to reply to the thread originator. The relevance of these features is estimated using a full likelihood approach and allows to characterise the habits and communication patterns of a given platform and/or community. We apply the proposed framework on four popular websites: Slashdot, Barrapunto (a Spanish version of Slashdot), Meneame (a Spanish Digg-clone) and the article discussion pages of the English Wikipedia. Our results provide significant insight into understanding how discussion cascades grow and have potential applications in broader contexts such as community management or design of communication platforms.
publishDate 2013
dc.date.none.fl_str_mv 2013
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/26746
http://dx.doi.org/10.1007/s11280-012-0162-8
url http://hdl.handle.net/10230/26746
http://dx.doi.org/10.1007/s11280-012-0162-8
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv World Wide Web. 2013;16(5):645-75.
http://hdl.handle.net/10230/26270
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
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