Algorithmic Amplification of Politics on Twitter
- URL: http://arxiv.org/abs/2110.11010v1
- Date: Thu, 21 Oct 2021 09:25:39 GMT
- Title: Algorithmic Amplification of Politics on Twitter
- Authors: Ferenc Husz\'ar, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew
Schlaikjer and Moritz Hardt
- Abstract summary: We provide quantitative evidence from a massive-scale randomized experiment on the Twitter platform.
We studied Tweets by elected legislators from major political parties in 7 countries.
In 6 out of 7 countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left.
- Score: 17.631887805091733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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