Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles
- Autores
- Bazán Pereyra, Gerónimo; Cuello, Carolina; Capodici, Gianfranco; Jofré, Vanessa; Ferretti, Edgardo; Errecalde, Marcelo Luis
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically assessing the need of including additional citations for contributing to verify the articles’ content; the so-called Refimprove quality flaw. This information quality flaw, ranks among the five most frequent flaws and represents 12.4% of the flawed articles in the English Wikipedia. Underbagged decision trees, biased-SVM, and centroid-based balanced SVM –three different state-of-the-art approaches– were evaluated, with the aim of handling the existing imbalances between the number of articles’ tagged as flawed content, and the remaining untagged documents that exist in Wikipedia, which can help in the learning stage of the algorithms. Also, a uniformly sampled balanced SVM classifier was evaluated as a baseline. The results showed that under-bagged decision trees with the min rule as aggregation method, perform best achieving an F1 score of 0.96 on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Likewise, biased-SVM also achieved an F1 score that outperform previously published results.
II Track de Gobierno Digital y Ciudades Inteligentes.
Red de Universidades con Carreras en Informática - Materia
-
Ciencias Informáticas
Wikipedia
Information Quality
Quality Flaws Prediction
Refimprove Flaw - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/90453
Ver los metadatos del registro completo
id |
SEDICI_487ee57fda82f7412745965970a40971 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/90453 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia ArticlesBazán Pereyra, GerónimoCuello, CarolinaCapodici, GianfrancoJofré, VanessaFerretti, EdgardoErrecalde, Marcelo LuisCiencias InformáticasWikipediaInformation QualityQuality Flaws PredictionRefimprove FlawQuality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically assessing the need of including additional citations for contributing to verify the articles’ content; the so-called Refimprove quality flaw. This information quality flaw, ranks among the five most frequent flaws and represents 12.4% of the flawed articles in the English Wikipedia. Underbagged decision trees, biased-SVM, and centroid-based balanced SVM –three different state-of-the-art approaches– were evaluated, with the aim of handling the existing imbalances between the number of articles’ tagged as flawed content, and the remaining untagged documents that exist in Wikipedia, which can help in the learning stage of the algorithms. Also, a uniformly sampled balanced SVM classifier was evaluated as a baseline. The results showed that under-bagged decision trees with the min rule as aggregation method, perform best achieving an F1 score of 0.96 on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Likewise, biased-SVM also achieved an F1 score that outperform previously published results.II Track de Gobierno Digital y Ciudades Inteligentes.Red de Universidades con Carreras en Informática2019-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf42-51http://sedici.unlp.edu.ar/handle/10915/90453enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1info:eu-repo/semantics/reference/hdl/10915/90359info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:18:37Zoai:sedici.unlp.edu.ar:10915/90453Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:18:37.798SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
title |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
spellingShingle |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles Bazán Pereyra, Gerónimo Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Refimprove Flaw |
title_short |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
title_full |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
title_fullStr |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
title_full_unstemmed |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
title_sort |
Automatically Assessing the Need of Additional Citations for Information Quality Verification in Wikipedia Articles |
dc.creator.none.fl_str_mv |
Bazán Pereyra, Gerónimo Cuello, Carolina Capodici, Gianfranco Jofré, Vanessa Ferretti, Edgardo Errecalde, Marcelo Luis |
author |
Bazán Pereyra, Gerónimo |
author_facet |
Bazán Pereyra, Gerónimo Cuello, Carolina Capodici, Gianfranco Jofré, Vanessa Ferretti, Edgardo Errecalde, Marcelo Luis |
author_role |
author |
author2 |
Cuello, Carolina Capodici, Gianfranco Jofré, Vanessa Ferretti, Edgardo Errecalde, Marcelo Luis |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Refimprove Flaw |
topic |
Ciencias Informáticas Wikipedia Information Quality Quality Flaws Prediction Refimprove Flaw |
dc.description.none.fl_txt_mv |
Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically assessing the need of including additional citations for contributing to verify the articles’ content; the so-called Refimprove quality flaw. This information quality flaw, ranks among the five most frequent flaws and represents 12.4% of the flawed articles in the English Wikipedia. Underbagged decision trees, biased-SVM, and centroid-based balanced SVM –three different state-of-the-art approaches– were evaluated, with the aim of handling the existing imbalances between the number of articles’ tagged as flawed content, and the remaining untagged documents that exist in Wikipedia, which can help in the learning stage of the algorithms. Also, a uniformly sampled balanced SVM classifier was evaluated as a baseline. The results showed that under-bagged decision trees with the min rule as aggregation method, perform best achieving an F1 score of 0.96 on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Likewise, biased-SVM also achieved an F1 score that outperform previously published results. II Track de Gobierno Digital y Ciudades Inteligentes. Red de Universidades con Carreras en Informática |
description |
Quality flaws prediction in Wikipedia is an ongoing research trend. In particular, in this work we tackle the problem of automatically assessing the need of including additional citations for contributing to verify the articles’ content; the so-called Refimprove quality flaw. This information quality flaw, ranks among the five most frequent flaws and represents 12.4% of the flawed articles in the English Wikipedia. Underbagged decision trees, biased-SVM, and centroid-based balanced SVM –three different state-of-the-art approaches– were evaluated, with the aim of handling the existing imbalances between the number of articles’ tagged as flawed content, and the remaining untagged documents that exist in Wikipedia, which can help in the learning stage of the algorithms. Also, a uniformly sampled balanced SVM classifier was evaluated as a baseline. The results showed that under-bagged decision trees with the min rule as aggregation method, perform best achieving an F1 score of 0.96 on the test corpus from the 1st International Competition on Quality Flaw Prediction in Wikipedia; a well-known uniform evaluation corpus from this research field. Likewise, biased-SVM also achieved an F1 score that outperform previously published results. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/90453 |
url |
http://sedici.unlp.edu.ar/handle/10915/90453 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-987-688-377-1 info:eu-repo/semantics/reference/hdl/10915/90359 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 42-51 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616059756216320 |
score |
13.070432 |