Analyzing the impact of filter bubbles on social network polarization

Uthsav Chitra, Christopher Musco

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    While social networks have increased the diversity of ideas and information available to users, they are also blamed for increasing the polarization of user opinions. Eli Pariser’s “filter bubble” hypothesis [55] explains this counterintuitive phenomenon by linking user polarization to algorithmic filtering: to increase user engagement, social media companies connect users with ideas they are already likely to agree with, thus creating echo chambers of users with very similar beliefs. In this paper, we introduce a mathematical framework to assess the impact of this popular, yet unverified, hypothesis. We augment the classical Friedkin-Johnsen opinion dynamics model to include algorithmic filtering by introducing a network administrator — an external actor that models social media companies by dynamically adjusting the strength of edges in a social network graph. When the network administrator is incentivized to reduce disagreement among interacting users, we experimentally demonstrate on networks from Reddit and Twitter that even small changes by the administrator to social network graphs can increase user polarization. We support our experiments with theoretical results by showing that social networks generated from the stochastic block model are provably sensitive to algorithmic filtering. Finally, we propose a simple modification to the incentives of the network administrator that limits the filter bubble effect without significantly affecting user engagement.

    Original languageEnglish (US)
    Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
    PublisherAssociation for Computing Machinery, Inc
    Pages115-123
    Number of pages9
    ISBN (Electronic)9781450368223
    DOIs
    StatePublished - Jan 20 2020
    Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
    Duration: Feb 3 2020Feb 7 2020

    Publication series

    NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

    Conference

    Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
    CountryUnited States
    CityHouston
    Period2/3/202/7/20

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Software
    • Computer Science Applications

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  • Cite this

    Chitra, U., & Musco, C. (2020). Analyzing the impact of filter bubbles on social network polarization. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 115-123). (WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3336191.3371825