Algorithmic amplification of politics on Twitter

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.

Political content is a major part of the public conversation on Twitter. Politicians, political organizations, and news outlets engage large audiences on Twitter. At the same time, Twitter employs algorithms that learn from data to sort content on the platform. This interplay of algorithmic content curation and political discourse has been the subject of intense scholarly debate and public scrutiny (1–15). When first established as a service, Twitter used to present individuals with content from accounts they followed, arranged in a reverse chronological feed. In 2016, Twitter introduced machine learning algorithms to render tweets on this feed called Home timeline based on a personalized relevance model (16). Individuals would now see older tweets deemed relevant to them, as well as some tweets from accounts they did not directly follow.Personalized ranking prioritizes some tweets over others on the basis of content features, social connectivity, and user activity. There is evidence that different political groups use Twitter differently to achieve political goals (17–20). What has remained a matter of debate, however, is whether or not any ranking advantage falls along established political contours, such as the left or right (2, 7), the center or the extremes (1, 3), specific parties (2, 7), or news sources of a certain political inclination (21). In this work, we provide systematic quantitative insights into this question based on a massive-scale randomized experiment on the Twitter platform.