Automatic Detection of Influencers in Social Networks: Authority versus Domain signals

Rodríguez-Vidal, Javier, Gonzalo, Julio, Plaza, Laura y Anaya Sánchez, Henry . (2019) Automatic Detection of Influencers in Social Networks: Authority versus Domain signals. Journal of the Association for Information Science and Technology Vol 70 (7), p. 675-684

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Rodriguez_Vidal_Javier_Automatic_Detection_In.pdf Rodriguez Vidal_Javier_Automatic Detection In.pdf application/pdf 374.29KB

Título Automatic Detection of Influencers in Social Networks: Authority versus Domain signals
Autor(es) Rodríguez-Vidal, Javier
Gonzalo, Julio
Plaza, Laura
Anaya Sánchez, Henry
Materia(s) Informática
Abstract Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.
Palabras clave Learning to Rank
Web and social media search
Information extraction
Social Network Analysis
Natural Language Processing
Social Media Influencers
Editor(es) Wiley
Fecha 2019-01-07
Formato application/pdf
Identificador bibliuned:DptoLSI-ETSI-GPLNyRI-Jrodriguez-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-GPLNyRI-Jrodriguez-0001
DOI - identifier https://doi.org/10.1002/asi.24156
ISSN - identifier 2330-1643, 2330-1635
Nombre de la revista Journal of the Association for Information Science and Technology
Número de Volumen 70
Número de Issue 7
Página inicial 675
Página final 684
Publicado en la Revista Journal of the Association for Information Science and Technology Vol 70 (7), p. 675-684
Idioma eng
Versión de la publicación acceptedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales This is an Accepted Manuscript of an article published by Wiley in "Journal of the Association for Information Science and Technology Vol 70 (7), p. 675-684", available at: https://doi.org/10.1002/asi.24156
Notas adicionales Este es el manuscrito aceptado del artículo publicado por Wiley en "Journal of the Association for Information Science and Technology Vol 70 (7), p. 675-684", disponible en línea: https://doi.org/10.1002/asi.24156

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 38 Visitas, 11 Descargas  -  Estadísticas en detalle
Creado: Fri, 26 Jan 2024, 20:45:49 CET