Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources

[EN] Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers....

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Detalles Bibliográficos
Autores: Munar, Antoni, Chiner, Esteban
Tipo de recurso: capítulo de libro
Fecha de publicación:2016
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/84787
Acceso en línea:https://riunet.upv.es/handle/10251/84787
Access Level:acceso abierto
Palabra clave:web data
internet data
big data
qca
pls
sem
conference
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oai_identifier_str oai:riunet.upv.es:10251/84787
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repository_id_str
spelling Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sourcesMunar, AntoniChiner, Estebanweb datainternet databig dataqcaplssemconference[EN] Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers. At the same time, some of the more widespread social network platforms seem to be evolving not only as social networks between individuals but also as mass information distribution media. When knowing your customer through social networking services, it may be of interest to disambiguate which part of the customer context in the network relates to his peers from other sources. In this paper we present an algorithmic approach to disambiguate one aspect of such relation, as expressed in the nature of the contacts established in the social network: with peers or with organizations, news media or influencers. We focus in the case of Twitter where a simple supervised linear regression can provide a ranking score, effectively discriminating and ordering by closeness peer and other types of contacts (mass media or influencers). Such discrimination can serve as a preliminary step for deeper analysis or privacy protection of customer interaction and is suitable for implementation in automated Big Data systems.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20162016-10-10book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/84787reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/847872026-06-13T07:49:27Z
dc.title.none.fl_str_mv Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
title Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
spellingShingle Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
Munar, Antoni
web data
internet data
big data
qca
pls
sem
conference
title_short Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
title_full Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
title_fullStr Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
title_full_unstemmed Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
title_sort Know your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
dc.creator.none.fl_str_mv Munar, Antoni
Chiner, Esteban
author Munar, Antoni
author_facet Munar, Antoni
Chiner, Esteban
author_role author
author2 Chiner, Esteban
author2_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv web data
internet data
big data
qca
pls
sem
conference
topic web data
internet data
big data
qca
pls
sem
conference
description [EN] Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers. At the same time, some of the more widespread social network platforms seem to be evolving not only as social networks between individuals but also as mass information distribution media. When knowing your customer through social networking services, it may be of interest to disambiguate which part of the customer context in the network relates to his peers from other sources. In this paper we present an algorithmic approach to disambiguate one aspect of such relation, as expressed in the nature of the contacts established in the social network: with peers or with organizations, news media or influencers. We focus in the case of Twitter where a simple supervised linear regression can provide a ranking score, effectively discriminating and ordering by closeness peer and other types of contacts (mass media or influencers). Such discrimination can serve as a preliminary step for deeper analysis or privacy protection of customer interaction and is suitable for implementation in automated Big Data systems.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-10-10
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/84787
url https://riunet.upv.es/handle/10251/84787
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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