An interaction-aware approach for social influence maximization

Autores
Alonso, Diego Gabriel; Monteserin, Ariel José; Berdun, Luis Sebastian
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Microblogging networks are considered a great source of social influence. One of its characteristics is their high dynamism. This fact produces that influential users continuously change according with time and topic. Several social networks metrics have been defined to rank influential users. However, these metrics fail to capture the dynamism of microblogging networks. For this reason, we propose an approach based on Credit Distribution model to identify the influential users of a microblogging social network by performing an online analysis of the users’ interactions. Moreover, we present a comparison of our approach with well-known metrics used for influencers ranking. The experiments were carried out in Twitter during sport events (football matches) and new product (video games) launchings. The results showed that our approach outperforms the metric-based rankings in terms of the influence spread. This confirms the importance of being updated for identifying influential users.
Fil: Alonso, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
Social Influence Maximization
Social Network Modeling
Influencers Discovering
Viral Marketing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/231425
Acceder
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network_name_str CONICET Digital (CONICET)
spelling An interaction-aware approach for social influence maximizationAlonso, Diego GabrielMonteserin, Ariel JoséBerdun, Luis SebastianSocial Influence MaximizationSocial Network ModelingInfluencers DiscoveringViral Marketinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Microblogging networks are considered a great source of social influence. One of its characteristics is their high dynamism. This fact produces that influential users continuously change according with time and topic. Several social networks metrics have been defined to rank influential users. However, these metrics fail to capture the dynamism of microblogging networks. For this reason, we propose an approach based on Credit Distribution model to identify the influential users of a microblogging social network by performing an online analysis of the users’ interactions. Moreover, we present a comparison of our approach with well-known metrics used for influencers ranking. The experiments were carried out in Twitter during sport events (football matches) and new product (video games) launchings. The results showed that our approach outperforms the metric-based rankings in terms of the influence spread. This confirms the importance of being updated for identifying influential users.Fil: Alonso, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaInstitute of Electrical and Electronics Engineers2023-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/231425Alonso, Diego Gabriel; Monteserin, Ariel José; Berdun, Luis Sebastian; An interaction-aware approach for social influence maximization; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 11; 9-2023; 1171-11801548-0992CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/7022info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T10:45:41Zoai:ri.conicet.gov.ar:11336/231425instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-17 10:45:41.392CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An interaction-aware approach for social influence maximization
title An interaction-aware approach for social influence maximization
spellingShingle An interaction-aware approach for social influence maximization
Alonso, Diego Gabriel
Social Influence Maximization
Social Network Modeling
Influencers Discovering
Viral Marketing
title_short An interaction-aware approach for social influence maximization
title_full An interaction-aware approach for social influence maximization
title_fullStr An interaction-aware approach for social influence maximization
title_full_unstemmed An interaction-aware approach for social influence maximization
title_sort An interaction-aware approach for social influence maximization
dc.creator.none.fl_str_mv Alonso, Diego Gabriel
Monteserin, Ariel José
Berdun, Luis Sebastian
author Alonso, Diego Gabriel
author_facet Alonso, Diego Gabriel
Monteserin, Ariel José
Berdun, Luis Sebastian
author_role author
author2 Monteserin, Ariel José
Berdun, Luis Sebastian
author2_role author
author
dc.subject.none.fl_str_mv Social Influence Maximization
Social Network Modeling
Influencers Discovering
Viral Marketing
topic Social Influence Maximization
Social Network Modeling
Influencers Discovering
Viral Marketing
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Microblogging networks are considered a great source of social influence. One of its characteristics is their high dynamism. This fact produces that influential users continuously change according with time and topic. Several social networks metrics have been defined to rank influential users. However, these metrics fail to capture the dynamism of microblogging networks. For this reason, we propose an approach based on Credit Distribution model to identify the influential users of a microblogging social network by performing an online analysis of the users’ interactions. Moreover, we present a comparison of our approach with well-known metrics used for influencers ranking. The experiments were carried out in Twitter during sport events (football matches) and new product (video games) launchings. The results showed that our approach outperforms the metric-based rankings in terms of the influence spread. This confirms the importance of being updated for identifying influential users.
Fil: Alonso, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Microblogging networks are considered a great source of social influence. One of its characteristics is their high dynamism. This fact produces that influential users continuously change according with time and topic. Several social networks metrics have been defined to rank influential users. However, these metrics fail to capture the dynamism of microblogging networks. For this reason, we propose an approach based on Credit Distribution model to identify the influential users of a microblogging social network by performing an online analysis of the users’ interactions. Moreover, we present a comparison of our approach with well-known metrics used for influencers ranking. The experiments were carried out in Twitter during sport events (football matches) and new product (video games) launchings. The results showed that our approach outperforms the metric-based rankings in terms of the influence spread. This confirms the importance of being updated for identifying influential users.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/231425
Alonso, Diego Gabriel; Monteserin, Ariel José; Berdun, Luis Sebastian; An interaction-aware approach for social influence maximization; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 11; 9-2023; 1171-1180
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/231425
identifier_str_mv Alonso, Diego Gabriel; Monteserin, Ariel José; Berdun, Luis Sebastian; An interaction-aware approach for social influence maximization; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 21; 11; 9-2023; 1171-1180
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/7022
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1843606010413973504
score 13.001348

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