Study: Algorithmic Amplification of Politics on Twitter

Image is illustrative. Photo by Cristian Dina from Pexels

The authors of the study summarise: “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.” In this large scale study, authors 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,” authors say in the abstract of the study. The authors further looked at whether algorithms amplify far- left and far-right political groups more than moderate ones amd contrary to prevailing public belief, they did not find evidence to support this hypothesis.

The authors’ aim is to contribute to an evidence-based debate on the role personalisation algorithms play in shaping political content consumption.


Ferenc Huszár
Ferenc Huszár

I'm Ferenc Huszár, Senior Lecturer in Machine Learning at the University of Cambridge. I recently joined the Department of Computer Science and Technology, a nice and cozy department, where we're building a new machine learning group with Neil Lawrence and Carl Henrik Ek and others. I'm interested in principled deep learning techniques: optimization, generalization, representation, transfer, meta-learning, and so on.

Sofia Ira Ktena

Following my PhD I worked in various jobs in the London tech/startup sector. My highlight as a researcher is joining Magic Pony Technology, a startup where we developed deep learning-based image superresolution] and compression techniques. After Twitter's acquisition Magic Pony, I have worked on a range of ML topics, like recommender systems and fair machine learning; LucaBelli: Mathematician turned Deep Learning engineer. Currently at Twitter Cortex; Moritz Hardt: Director of Social Foundations of Computation at Max Planck Institute for Intelligent Systems, Tübingen (starting January 1, 2022).

Conor O’Brien

Luca Belli

Andrew Schlaikjera

Moritz Hardt
Moritz Hardt


Computer Laboratory, University of Cambridge, Cambridge, UK

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA