%0 Journal Article
%T Role detection in online forums based on growth models for trees
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%+ Institut de Mathématiques de Toulouse UMR5219 (IMT)
%+ Technicolor R & I [Cesson Sévigné]
%A Lumbreras, Alberto
%A Jouve, Bertrand
%A Velcin, Julien
%A Guégan, Marie
%< avec comité de lecture
%@ 1869-5450
%J Social Network Analysis and Mining
%I Springer
%V 7
%N 1
%P 49
%8 2017
%D 2017
%R 10.1007/s13278-017-0472-z
%K Growth models
%K Clustering
%K Role detection
%K Social network
%K discussion Forums
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Web
%Z Statistics [stat]/Machine Learning [stat.ML]
%Z Computer Science [cs]/Social and Information Networks [cs.SI]Journal articles
%X Some structural characteristics of online discussions have been successfully modeled in the recent years. When parameters of these models are properly estimated, the models are able to generate synthetic discussions that are structurally similar to the real discussions. A common aspect of these models is that they consider that all users behave according to the same model. In this paper, we combine a growth-model with an Expectation-Maximization algorithm that finds different parameters for different latent groups of users. We use this method to find the different roles that coexist in the community. Moreover, we analyze whether we can predict users behaviors based on their roles. Indeed, we show that predictions are improved for some of the roles when compared with a simple growth model.
%G English
%2 https://hal.science/hal-01665539/document
%2 https://hal.science/hal-01665539/file/SNAM_paper_preprint.pdf
%L hal-01665539
%U https://hal.science/hal-01665539
%~ UNIV-TLSE2
%~ UNIV-TLSE3
%~ CNRS
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ INSA-TOULOUSE
%~ ERIC
%~ IMT
%~ UT1-CAPITOLE
%~ LABEXIMU
%~ LYON2
%~ INSA-GROUPE
%~ UDL
%~ UNIV-LYON
%~ TEST-HALCNRS
%~ UNIV-UT3
%~ UT3-INP
%~ UT3-TOULOUSEINP