Filter bubble

By Axel Bruns. Internet Policy Review, 2019

455 citations recorded by the source corpus.

: Introduced by tech entrepreneur and activist Eli Pariser in 2011, the ‘filter bubble’ is a persistent concept which suggests that search engines and social media, together with their recommendation and personalisation algorithms, are centrally culpable for the societal and ideological polarisation experienced in many countries: we no longer encounter a balanced and healthy information diet, but only see information that targets our established interests and reinforces our existing worldviews. Filter bubbles are seen as critical enablers of Brexit, Trump, Bolsonaro, and other populist political phenomena, and search and social media companies have been criticised for failing to prevent their development. Yet, there is scant empirical evidence for their existence, or for the related concept of ‘echo chambers’: indeed, search and social media users generally appear to encounter a highly centrist media diet that is, if anything, more diverse than that of non-users. However, the persistent use of these concepts in mainstream media and political debates has now created its own discursive reality that continues to impact materially on societal institutions, media and communication platforms, and ordinary users themselves. This article provides a critical review of the ‘filter bubble’ idea, and concludes that its persistence has served only to redirect scholarly attention from far more critical areas of enquiry.

Subfields: Communication, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/2a82be0a18b5742794f3c5785719e1f709639110

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. L. Elisa Celis
2. Nisheeth K. Vishnoi
3. Natali Helberger

In Computer Science Applications

1. L. Elisa Celis
2. Nisheeth K. Vishnoi
3. Natali Helberger

In Information Systems

1. Nisheeth K. Vishnoi
2. L. Elisa Celis
3. Lucia D'Acunto
4. Natali Helberger
5. Kari Karppinen

Citations in the corpus

• Controlling Polarization in Personalization: An Algorithmic Framework (2019, background): “, enabled by polarized information, many people did not expect a Brexit vote or Trump election [10].”
• Exposure diversity as a design principle for recommender systems (2018, background): “This warning mirrors the debates on ‘filter bubbles’ (Pariser, 2011), ‘echo chambers’, ‘information cocoons’ (Sunstein, 2007) and other concerns in the academic and public debates over the fragmentation of public discourse.”