A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions

Twitter is one of the most powerful social media platforms, reflecting both support and contrary opinions among people who use it. In a recent work, we developed an argumentative approach for analyzing the major opinions accepted and rejected in Twitter discussions. A Twitter discussion is modeled a...

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Detalles Bibliográficos
Autores: Alsinet, Teresa, Argelich Romà, Josep, Béjar Torres, Ramón, Cemeli Sánchez, Joel
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/67673
Acceso en línea:https://doi.org/10.1007/s00500-018-3380-x
http://hdl.handle.net/10459.1/67673
Access Level:acceso abierto
Palabra clave:Twitter discussions
Valued argumentation
Probability values
Distributed algorithm
Tractable cases
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spelling A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussionsAlsinet, TeresaArgelich Romà, JosepBéjar Torres, RamónCemeli Sánchez, JoelTwitter discussionsValued argumentationProbability valuesDistributed algorithmTractable casesTwitter is one of the most powerful social media platforms, reflecting both support and contrary opinions among people who use it. In a recent work, we developed an argumentative approach for analyzing the major opinions accepted and rejected in Twitter discussions. A Twitter discussion is modeled as a weighted argumentation graph where each node denotes a tweet, each edge denotes a relationship between a pair of tweets of the discussion and each node is attached to a weight that denotes the social relevance of the corresponding tweet in the discussion. In the social network Twitter, a tweet always refers to previous tweets in the discussion, and therefore the underlying argument graph obtained is acyclic. However, when in a discussion we group the tweets by author, the graph that we obtain can contain cycles. Based on the structure of graphs, in this work we introduce a distributed algorithm to compute the set of globally accepted opinions of a Twitter discussion based on valued argumentation. To understand the usefulness of our distributed algorithm, we study cases of argumentation graphs that can be solved efficiently with it. Finally, we present an experimental investigation that shows that when solving acyclic argumentation graphs associated with Twitter discussions our algorithm scales at most with linear time with respect to the size of the discussion. For argumentation graphs with cycles, we study tractable cases and we analyze how frequent are these cases in Twitter. Moreover, for the non-tractable cases we analyze how close is the solution of the distributed algorithm with respect to the one computed with the general sequential algorithm, that we have previously developed, that solves any argumentation graph.This work was partially funded by Spanish Project TIN2015-71799-C2-2-P (MINECO/FEDER).Springer2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://doi.org/10.1007/s00500-018-3380-xhttp://hdl.handle.net/10459.1/67673reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-PReproducció del document publicat a https://doi.org/10.1007/s00500-018-3380-xSoft Computing, 2019, vol. 23, núm,. 7, p. 2147–2166(c) Springer-Verlag GmbH Germany, part of Springer Nature, 2018info:eu-repo/semantics/openAccessoai:repositori.udl.cat:10459.1/676732026-06-24T12:42:17Z
dc.title.none.fl_str_mv A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
title A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
spellingShingle A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
Alsinet, Teresa
Twitter discussions
Valued argumentation
Probability values
Distributed algorithm
Tractable cases
title_short A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
title_full A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
title_fullStr A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
title_full_unstemmed A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
title_sort A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions
dc.creator.none.fl_str_mv Alsinet, Teresa
Argelich Romà, Josep
Béjar Torres, Ramón
Cemeli Sánchez, Joel
author Alsinet, Teresa
author_facet Alsinet, Teresa
Argelich Romà, Josep
Béjar Torres, Ramón
Cemeli Sánchez, Joel
author_role author
author2 Argelich Romà, Josep
Béjar Torres, Ramón
Cemeli Sánchez, Joel
author2_role author
author
author
dc.subject.none.fl_str_mv Twitter discussions
Valued argumentation
Probability values
Distributed algorithm
Tractable cases
topic Twitter discussions
Valued argumentation
Probability values
Distributed algorithm
Tractable cases
description Twitter is one of the most powerful social media platforms, reflecting both support and contrary opinions among people who use it. In a recent work, we developed an argumentative approach for analyzing the major opinions accepted and rejected in Twitter discussions. A Twitter discussion is modeled as a weighted argumentation graph where each node denotes a tweet, each edge denotes a relationship between a pair of tweets of the discussion and each node is attached to a weight that denotes the social relevance of the corresponding tweet in the discussion. In the social network Twitter, a tweet always refers to previous tweets in the discussion, and therefore the underlying argument graph obtained is acyclic. However, when in a discussion we group the tweets by author, the graph that we obtain can contain cycles. Based on the structure of graphs, in this work we introduce a distributed algorithm to compute the set of globally accepted opinions of a Twitter discussion based on valued argumentation. To understand the usefulness of our distributed algorithm, we study cases of argumentation graphs that can be solved efficiently with it. Finally, we present an experimental investigation that shows that when solving acyclic argumentation graphs associated with Twitter discussions our algorithm scales at most with linear time with respect to the size of the discussion. For argumentation graphs with cycles, we study tractable cases and we analyze how frequent are these cases in Twitter. Moreover, for the non-tractable cases we analyze how close is the solution of the distributed algorithm with respect to the one computed with the general sequential algorithm, that we have previously developed, that solves any argumentation graph.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s00500-018-3380-x
http://hdl.handle.net/10459.1/67673
url https://doi.org/10.1007/s00500-018-3380-x
http://hdl.handle.net/10459.1/67673
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-P
Reproducció del document publicat a https://doi.org/10.1007/s00500-018-3380-x
Soft Computing, 2019, vol. 23, núm,. 7, p. 2147–2166
dc.rights.none.fl_str_mv (c) Springer-Verlag GmbH Germany, part of Springer Nature, 2018
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Springer-Verlag GmbH Germany, part of Springer Nature, 2018
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
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