A Multi-Factorial Analysis of Polarization on Social Media

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 metrics, although defined in a continuous space, often lead to a unimodal distribution of data once applied to users' interactions, making the distinction between polarized and non-polarized users difficult to draw. Furthermore, each metric relies on a single factor and does not reflect the overall user behavior. Modeling polarization in a single form runs the risk of obscuring inter-individual differences. In this paper, we propose to have a deeper look at polarized online behaviors and to compare individual metrics. We collected about 300K retweets from 1K French users between January and July 2022 on Twitter. Each retweet is related to the highly controversial vaccine debate. Results show that a multi-factorial analysis leads to the identification of distinct and potentially explainable behavioral classes. This finer understanding of behaviors is an essential step to adapt news recommendation strategies so that no user gets locked into an echo chamber or filter bubble

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

Polarization is often a clich{\'e}, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social networks, polarization nevertheless remains a complex social phenomenon that must be placed in a wider context. To contribute to a better understanding of polarization, we approach it as an evolving process, drawing on a dual expertise in political and data sciences. We compare the polarization process between one mature debate (COVID-19 vaccine) and one emerging debate (Ukraine conflict) at the time of data collection. Both debates are studied on Twitter users, a highly politicized population, and on the French population to provide key elements beyond the traditional US context. This unprecedented analysis confirms that polarization varies over time, through a succession of specific periods, whose existence and duration depend on the maturity of the debate. Importantly, we highlight that polarization is paced by context-related events. Bearing this in mind, we pave the way for a new generation of personalized depolarization strategies, adapted to the context and maturity of debates

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

International audienceThe number of resources or items that users can now access when navigating on the Web or using e-services, is so huge that these might feel lost due to the presence of too much information. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users' needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that does not use any a priori information about the users, nor any data about the content of the items. Collaborative filtering relies on the preferences of items expressed by users. These are usually recorded under the form of ratings and the recommendation technique exploits these ratings. However, in many e-services, it is inappropriate to ask to rate items; it may indeed interrupt users' activity. In the absence of ratings, classical collaborative filtering techniques cannot be applied; especially the selection of like-minded users for a given user, also called his mentor users, cannot be performed. Fortunately, the behavior of users, such as their consultations, can be collected; this collection is transparent for users. In this paper, we focus on rating-free collaborative filtering: we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentor users of a given user. These algorithms have the advantage of not only being less complex than community detection algorithms, but also of discovering overlapping communities. We adapt one state of the art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted on the two datasets show that the precision achieved by this community detection algorithm is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a set of mentors reduced by 71\% and 99\% compared to the baseline

Utilité et perception de la diversité dans les systèmes de recommandation

National audienceDe récentes études ont montré que la diversité dans les systèmes de recommandation est positivement corrélée à la satisfaction des utilisateurs et renforce/facilite leur choix d'un item. Si l'impact de cette nouvelle dimension a été mesuré, les raisons d'un tel succès restent cependant encore inexpliquées. Forts de ce constat, notre objectif est d'analyser plus finement l'utilité réelle et perçue de la diversité dans les systèmes de recommandation. Dans cette optique, nous avons réalisé une étude auprès de 250 utilisateurs permettant de comparer 5 approches (mêlant filtrage collaboratif, filtrage par contenu et popularité) avec différents degrés de diversité. Les résultats montrent que la diversité dans les recommandations est perçue par les utilisateurs et améliore leur satisfaction, même si elle suscite parfois méfiance ou incompréhension. En outre, cette étude a mis en lumière la nécessité de constituer des modèles de préférences suffisamment divers pour générer de bonnes recommandations

When Diversity Is Needed... But Not Expected!

International audienceRecent studies have highlighted the correlation between users' satisfaction and diversity within recommenders, especially the fact that diversity increases users' confidence when choosing an item. Understanding the reasons of this positive impact on recommenders is now becoming crucial. Based on this assumption, we designed a user study that focuses on the utility of this new dimension, as well as its perceived qualities. This study has been conducted on 250 users and it compared 5 recommendation approaches, based on collaborative filtering, content-based filtering and popularity, along with various degrees of diversity. Results show that, when recommendations are made explicit, diversity may reduce users' acceptance rate. However, it helps increasing users' satisfaction. Moreover, this study highlights the need to build users' preference models that are diverse enough, so as to generate good recommendations

Probabilistic Association Rules for Item-Based Recommender Systems

International audienceSince the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users with data, but to improve the human/computer interactions in information systems by suggesting fair items at the right time. Modeling personal preferences enables recommender systems to identify relevant subsets of items. These systems often rely on filtering techniques based on symbolic or numerical approaches in a stochastic context. In this paper, we focus on item-based collaborative filtering (CF) techniques. We show that it may be difficult to guarantee a good accuracy for the high values of prediction when ratings are not enough shared out on the rating scale. Thus, we propose a new approach combining a classic CF algorithm with an item association model to get better predictions. We deal with this issue by exploiting probalistic skewnesses in triplets of items. We validate our model by using the MovieLens dataset and get a significant improvement as regards the High MAE measure

Extreme congestion of microswimmers at a bottleneck constriction

When attracted by a stimulus (e. g. light), microswimmers can build up very densely at a constriction and thus cause clogging. The micro-alga \textit{Chlamydomonas Reinhardtii} is used here as a model system to study this phenomenon. Its negative phototaxis makes the algae swim away from a light source and go through a microfabricated bottleneck-shaped constriction. Successive clogging events interspersed with bursts of algae are observed. A power law decrease is found to describe well the distribution of time lapses of blockages. Moreover, the evacuation time is found to increase when increasing the swimming velocity. These results might be related to the phenomenology of crowd dynamics and in particular what has been called the Faster is Slower effect in the dedicated literature. It also raises the question of the presence of tangential solid friction between motile cells densely packed that may accompany arches formation. Using the framework of crowd dynamics we analyze the microswimmers behavior and in particular question the role of hydrodynamics

Calcul de stabilité des berges d'un canal : Application au réseau de canaux de la Sèvre Niortaise

Les berges de la Sèvre Niortaise sont régulièrement endommagées par des glissements circulaires. Des chantiers de restauration utilisant des soutènements par pieux, des géotextiles et des plantations ont déjà été réalisés. L'étendue des dégradations étant importante, l'optimisation des travaux est devenue nécessaire. Ainsi, nous travaillons à la réalisation d'un outil de prédiction des évolutions topographiques du canal et des berges. Celui-ci sert à la proposition de solutions de restauration. Le paramétrage du modèle s'appuie sur des essais mécaniques réalisés sur des échantillons de sol prélevés in situ. Une berge située à Damvix (85) a été modélisée. Cette étude a permis de déterminer la géométrie des surfaces de rupture potentielles et de tester l'influence des différents facteurs déstabilisants