Algorithmic amplification of politics on Twitter

[essentialsaltes]

https://www.pnas.org/doi/full/10.1073/pnas.2025334119

(FWIW the authors are/were Twitter employees)

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 2 million 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 seven countries. Our results reveal a remarkably consistent trend: In six out of seven 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 US media landscape revealed that algorithmic amplification favors 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.

 

[durangodawood]

https://www.pnas.org/doi/full/10.1073/pnas.2025334119

(FWIW the authors are/were Twitter employees)

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 2 million 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 seven countries. Our results reveal a remarkably consistent trend: In six out of seven 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 US media landscape revealed that algorithmic amplification favors 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.

Is this an algorithm effect... or just the expected outcome given that right wing leaning people are on average more addicted to little hits of indignation and outrage - and so return reliably for more?

 

[essentialsaltes]

Is this an algorithm effect... or just the expected outcome given that right wing leaning people are on average more addicted to little hits of indignation and outrage - and so return reliably for more?

I think they tried to look only at the algorithm effect of Twitter inserting things into your feed that it thinks you might like. But you're right that there may be other biases at work.

Recent arguments that different political parties pursue different strategies on Twitter (14, 15) may provide an explanation as to why these disparities exist. However, understanding the precise causal mechanism that drives amplification invites further study that we hope our work initiates.

 

[Skye1300]

I believe it because I set my twitter feed to only show me certain content and I wanted NOTHING political. Next thing I know, I'm still getting content I don't want to see and some political stuff too. They set the algorithm to what thy want you to see.