Algorithmic Amplification of Politics on Twitter

TL;DR

This study provides empirical evidence that Twitter's personalization algorithms tend to amplify right-leaning political content more than left-leaning content across multiple countries, challenging assumptions about bias against moderates or extremes.

Contribution

The paper presents a large-scale randomized experiment showing consistent right-leaning amplification on Twitter, offering quantitative insights into algorithmic political bias.

Findings

Right-leaning content is more amplified than left-leaning in 6 of 7 countries.

Algorithmic amplification favors right-leaning news sources in the U.S.

No evidence found that far-left or far-right groups are more amplified than moderates.

Abstract

Content on Twitter's home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There's been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2M daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied Tweets by elected legislators from major political parties in 7 countries. Our results reveal a remarkably consistent trend: In 6 out of 7 countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the U.S. media landscape revealed that algorithmic amplification favours right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones: contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.