Generative models of online discussion threads

Last week, we published our survey Generative models of online discussion threads: state of the art and research challenges in the Journal of Internet Services and Applications. This article is a review of the state of the art in modeling the structure of online discussion, including a historical overview, empirical evidence of relevant social theories, and the description and applications of seven statistical models to reproduce the structure and growth of discussion threads.

The work was done with my thesis supervisors Andreas Kaltenbrunner and Vicenç Gómez and, in addition, this was also the first (and very enriching!) collaboration with David García. As every research piece of my PhD, this was supported by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502).

Modeling approach considered in the review: the model (box in the middle) represents a mechanism or procedure that describes how discussion threads are formed. It is usually governed by a set of parameters θ which are typically learned from real data composed of real discussion threads. This learning step involves some type of optimization. For given parameters θ, the model can be used to generate synthetic threads that reproduce the properties of the real discussion threads



Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. This interest has led to the development of statistical models which are able to characterize the dynamics of threaded online conversations.

In this paper, we review research on statistical modeling of online discussions, in particular, we describe current generative models of the structure and growth of discussion threads. These are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms. We aim to provide a clear overview of the state of the art and to motivate future work in this relevant research field.

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