Quantifying Echo Chambers and Their Impact on News Engagement: Evidence from a Facebook Algorithm Update
I show that social media platforms face incentives to make algorithmic design choices which amplify misinformation and polarization in news consumption – specifically, by increasing the prevalence of echo chambers. This study leverages a 2018 Facebook algorithm update to investigate how increased network homophily affects news spread in a social network. My findings support a model of news consumption on social media where rational consumers re-share news articles based on reputational concerns and a desire to spread factual news; I extend this model to analyze how network structure influences tribalism in news engagement choices. Using an empirical reworking of the model, I measure the magnitude of the increase in social network homophily caused by the algorithm update. I then use this credibly exogenous shift to test the model’s main predictions. As predicted, greater homophily increases engagement with less reliable, more divisive news, and intensifies tribalism of engagement behaviour with media via an ‘agitation bubble’ effect. The results demonstrate that echo chambers created by social media platforms can drive tribalism and misinformation, rather than merely reflecting the existing prevalence of these phenomena in society. As the algorithm update benefited the platform, the results further suggest that platform incentives in shaping network structure misalign with social welfare and clarify how the global shift toward news consumption via social media can damage news diets. This work underscores the role of the communication network structure in explaining polarization, shifting focus away from explanations based on changing beliefs or cognitive biases.
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