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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)

Abstract

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.

Keywords

Twitter Communities Filter bubble Recommender system 

References

  1. 1.
    Bakshy, E., Messing, S., Adamic, L.A.: Exposure to ideologically diverse news and opinion on facebook. Science 348(6239), 1130–1132 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI 2018, pp. 43–52 (1998)Google Scholar
  3. 3.
    Colleoni, E., Rozza, A., Arvidsson, A.: Echo chamber or public sphere? Predicting political orientation and measuring political homophily in twitter using big data. J. Commun. 64(2), 317–332 (2014)CrossRefGoogle Scholar
  4. 4.
    Dugué, N., Labatut, V., Perez, A.: A community role approach to assess social capitalists visibility in the Twitter network. SNAM 5(1), 26 (2015)Google Scholar
  5. 5.
    Flaxman, S., Goel, S., Rao, J.M.: Filter bubbles, echo chambers, and online news consumption. Public Opin. Q. 80(S1), 298–320 (2016)CrossRefGoogle Scholar
  6. 6.
    Garimella, K., De Francisci Morales, G., Gionis, A., Mathioudakis, M.: Reducing controversy by connecting opposing views. In: WSDM, pp. 81–90 (2017)Google Scholar
  7. 7.
    Garrett, R.K.: Echo chambers online?: Politically motivated selective exposure among internet news users. JCC 14(2), 265–285 (2009)MathSciNetGoogle Scholar
  8. 8.
    Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., Roy, D.: Me, my echo chamber, and I: introspection on social media polarization. CoRR abs/1803.01731 (2018)Google Scholar
  9. 9.
    Gini, C.: Variabilità e mutabilità. Libreria Eredi Virgilio Veschi (1912)Google Scholar
  10. 10.
  11. 11.
    Grossetti, Q., Constantin, C., du Mouza, C., Travers, N.: An homophily-based approach for fast post recommendation in microblogging systems. In: Proceedings International Conference on Extending Database Technology (EDBT), Austria, pp. 1–12 (2018)Google Scholar
  12. 12.
    Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Enhancement of the neutrality in recommendation. In: Decisions@ RecSys, pp. 8–14 (2012)Google Scholar
  13. 13.
    Hyung Kang, J., Lerman, K.: Using Lists to measure homophily on Twitter. In: AAAI, pp. 26–32 (2012)Google Scholar
  14. 14.
    Kwak, H., Lee, C., Park, H., Moon, S.B.: What is twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)Google Scholar
  15. 15.
    Leicht, E.A., Newman, M.E.J.: Community structure in directed networks. Phys. Rev. Lett. 100, 118–122 (2008)CrossRefGoogle Scholar
  16. 16.
    Munson, S.A., Resnick, P.: Presenting diverse political opinions: how and how much. In: Human Factors in Computing Systems, pp. 1457–1466. ACM (2010)Google Scholar
  17. 17.
    Nguyen, T.T., Hui, P., Harper, F.M., Terveen, L.G., Konstan, J.A.: Exploring the filter bubble: the effect of using recommender systems on content diversity. In: WWW, pp. 677–686 (2014)Google Scholar
  18. 18.
    Pariser, E.: Beware online “filter bubbles” (2011). https://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles
  19. 19.
    Sharma, A., Jiang, J., Bommannavar, P., Larson, B., Lin, J.: GraphJet: real-time content recommendations at Twitter. PVLDB 9(13), 1281–1292 (2016)Google Scholar

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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