Filter Bubbles, Echo Chambers, and Online News Consumption

By Seth R. Flaxman, Sharad Goel, Justin M. Rao. 2016

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

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

In Safety, Robustness & Interpretability

Citations in the corpus

Degenerate Feedback Loops in Recommender Systems (2019, background): “In the social sciences literature, Flaxman, Goel, and Rao (2016) found that online services are associated with increased political polarization between users as well as increased exposure to the less preferred side of political opinions.”
Social Media, News Consumption, and Polarization: Evidence from a Field Experiment (2020, methodology): “This can be interpreted as the square root of the expected square distance between the slant of news sites visited by two random participants in the sample (Flaxman et al., 2016):”
Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity (2018, unknown): “So far, diversity is mainly discussed and measured in terms of political leaning of news sources and stories (e.g. Flaxman & Rao, 2016), thereby neglecting that diversity is a multi-dimensional concept that also encompasses topic plurality, genre, or the plurality in tone (Helberger et al., 2018).”
Hidden Incentives for Auto-Induced Distributional Shift (2020, background): “In a study of 50,000 US-based internet users, Flaxman & Goel (2015) ﬁnd that two things increase with social media and search engine use: (1) exposure of an individual to opposing or different viewpoints, and (2) mean ideological distance between users.”
Facebook news and (de)polarization: reinforcing spirals in the 2016 US election (2018, background): “media news recommendations resulted in increasing exposure to counter-attitudinal information (Bakshy et al., 2015; Flaxman et al., 2016; Garrett, 2009; Garrett et al., 2014; Garrett & Stroud, 2014; Gentzkow & Shapiro, 2011; Messing & Westwood, 2014).”
Algorithmic amplification of politics on Twitter (2021, methodology): “To better understand the effects of personalization on political discourse, we extend our analysis to a broader domain of news content (30, 31).”
Nathaniel Persily e Joshua A. Tucker (a cura di). Social Media and Democracy. The State of the Field, Prospects for Reform (2021, background): “…would be associated with a narrowing of news diets where people are only fed more of the same, in fact most empirical research finds use of social media and search engines associated with more diverse news use (Flaxman, Goel, and Rao 2016; Dubois and Blank 2018; Fletcher and Nielsen 2018a).”