Due to their success, social network platforms are considered today as a major communication mean. In order to increase user engagement, they rely on recommender systems to personalize individual experience by filtering messages according to user interest and/or neighborhood. However some recent results exhibit that this personalization of content might increase the echo chamber effect and create filter bubbles. These filter bubbles restrain the diversity of opinions regarding the recommended content. In this paper, we first realize a thorough study of communities on a large Twitter dataset to quantify how recommender systems affect users' behavior and create filter bubbles. Then we propose the Community Aware Model (CAM) to counter the impact of different recommender systems on information consumption. Our results show that filter bubbles concern up to 10% of users and our model based on similarities between communities enhance recommender systems.
Purpose
Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content.
Design/methodology/approach
The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption.
Findings
The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations.
Originality/value
The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.
National audienceLes services de micro-blogging sont devenus récemment une source d’information importante. Cependant, victimes de leur succès, ils doivent actuellement gérer une quantité sans précédent d’informations générées par les utilisateurs. Il devient par conséquent difficile pour les utilisateurs de trouver dans ces services des contenus proches de leurs intérêts. Afin de recommander des utilisateurs à suivre sur un sujet donné, nous proposons dans cet article des scores basés sur la topologie du graphe social ainsi que sur le contenu textuel des microblogs. Pour permettre le passage à l’échelle, nous présentons une approche qui s’appuie sur l’utilisation de landmarks pour pré-calculer des recommandations pour certains comptes choisis dans le graphe. Nos expériences confirment la pertinence de notre score de recommandation par rapport à des approches existantes ainsi que le passage à l’échelle de notre algorithme basé sur l’utilisation des landmarks
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