Analyzing the Impact of Filter Bubbles on Social Network Polarization

2020/01/20 by Uthsav Chitra, Christopher Musco · 124 citations
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computer science #Computer vision #Counterintuitive #Filter (signal processing) #Human–computer interaction #Internet privacy #Opinion Dynamics and Social Influence #Optical filter #Optics #Phenomenon #Physics #Polarization (electrochemistry) #Polarizing filter #Quantum mechanics #Social Media and Politics #Social media #World Wide Web

paper · doi:10.1145/3336191.3371825

openalex publication_date 2020/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

Citations

• Exposure to ideologically diverse news and opinion on Facebook
• Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation
• Social influence and opinions
• Social Media, Network Heterogeneity, and Opinion Polarization
• Encountering “Difference” in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks
• Polarization and Partisan Selective Exposure
• The Politicization of Climate Change and Polarization in the American Public's Views of Global Warming, 2001–2010
• The Polarization of Contemporary American Politics
• Filter Bubbles, Echo Chambers, and Online News Consumption
• The filter bubble and its effect on online personal health information
• The triple‐filter bubble: Using agent‐based modelling to test a meta‐theoretical framework for the emergence of filter bubbles and echo chambers
• Modeling confirmation bias and polarization

Cited by

• An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies
• The Impact of External Sources on the Friedkin-Johnsen Model
• Echoes of Disagreement: Measuring Disparity in Social Consensus
• Filter bubble [wikipedia]

Related

• Understanding Filter Bubbles and Polarization in Social Networks
• Measuring Online Social Bubbles
• Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption
• A critical review of filter bubbles and a comparison with selective exposure
• Segregation Before Polarization: How Recommendation Strategies Shape Echo Chamber Pathways