activity
20162026
most citedToward a Robust Diversity-Based Model to Detect Changes of Context

4 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.HC2026

BIRD: A Museum Open Dataset Combining Behavior Patterns and Identity Types to Better Model Visitors' Experience

Alexanne Worm, Florian Marchal, Sylvain Castagnos

Lack of data is a recurring problem in Artificial Intelligence, as it is essential for training and validating models. This is particularly true in the field of cultural heritage,…

cs.SI2023★ 2 cited

All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media

Celina Treuillier, Sylvain Castagnos, Armelle Brun

Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strateg…

cs.SI2023

Gaining a better understanding of online polarization by approaching it as a dynamic process

Celina Treuillier, Sylvain Castagnos, Christèle Lagier +1

Polarization is often a clich{é}, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social…

cs.SI2023

A Multi-Factorial Analysis of Polarization on Social Media

Celina Treuillier, Sylvain Castagnos, Armelle Brun

Polarization is an increasingly worrying phenomenon within social media. Recent work has made it possible to detect and even quantify polarization. Nevertheless, the few existing m…

cs.CY2019

Learning Analytics Made in France: The METALproject

Armelle Brun, Geoffray Bonnin, Sylvain Castagnos +2

This paper presents the METAL project, an ongoing French open Learning Analytics (LA) project for secondary school, that aims at improving the quality of the learning process. The…

cs.IR2016★ 4 cited

Toward a Robust Diversity-Based Model to Detect Changes of Context

Sylvain Castagnos, Amaury L 'Huillier, Anne Boyer

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose…