List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).

List of publications acknowledging the funding in Scopus.

The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository   as well as at the UPF e-repository   



Back Tutorial - Generative models of online discussion threads

Date: March 3rd 2020 17:00 - 19:30

Location: Poblenou Campus, UPF (Roc Boronat 138, Barcelona). Auditorium


Free registration HERE (max. 60 participants)

Part of the Master in Intelligent Interactive Systems at DTIC-UPF



The slides of the tutorial

The recording and material of the tutorial will be available in this web.


This tutorial is based on the following review paper

Aragón, P., Gómez, V., Garcı́a, D. & Kaltenbrunner, A. Generative models of online discussion threads: state of the art and research challenges. J. Internet Serv. Appl. 8, 15 (2017). DOI 10.1186/s13174-017-0066-z


Online discussion is a core feature of numerous social media platforms and has attracted increasing attention from academia for different and relevant reasons, e.g., the resolution of problems in collaborative editing, question answering and e-learning platforms, the response of online communities to news events, online political and civic participation, etc. Discussions on the Internet commonly occur as a exchange of written messages among two or more participants. These conversations are often represented as threads, which are initiated by a user posting a starting message (a post) and then other users replies to either the post or the earlier replies. Given this sequential posting behavior, online discussion threads follow a tree network structure. Different modeling approaches have been proposed to identify the governing mechanisms of the network structure of threads. Statistical models of this type are aimed to reproduce the growth of discussion threads through different features, often related to human behavior. This is why they are usually called generative models: they do not only estimate the statistical significance of their corresponding features but also reproduce the temporal arrival patterns of messages that form a discussion thread. The parameters of these models allow to compare different platforms and communities, they even can help to assess the impact of design choices and user interface changes on the way the discussions unfold. Therefore, we aim to provide the participants with state of the art tools and methods for the analysis, diagnosis, management and improvement of online discussion platform and communities.