Abstract
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.
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Notes
In practice, \(\mathbf{X }\) may be represented as a matrix of feature vectors \({\mathbf{x }}_i\) that makes the computing of the log-likelihood easy to vectorize in some programming languages.
In general, \(f({\mathbb {E}}[X]) \ge {\mathbb {E}}[f(X)]\) where f is a concave function.
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Lumbreras, A., Jouve, B., Velcin, J. et al. Role detection in online forums based on growth models for trees. Soc. Netw. Anal. Min. 7, 49 (2017). https://doi.org/10.1007/s13278-017-0472-z
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DOI: https://doi.org/10.1007/s13278-017-0472-z