Community-Based Recommendations on Twitter: Avoiding the Filter Bubble

  • Quentin Grossetti
  • Cédric du Mouza
  • Nicolas TraversEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


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.


Twitter Communities Filter bubble Recommender system 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Quentin Grossetti
    • 1
  • Cédric du Mouza
    • 1
  • Nicolas Travers
    • 1
    • 2
    Email author
  1. 1.CEDRIC Lab, CNAM ParisParisFrance
  2. 2.Research CenterLéonard de Vinci Pôle UniversitaireParisFrance

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