The social media platform examined tweets from elected officials in seven countries: the UK, US, Canada, France, Germany, Spain and Japan. It also studied whether political content from news organisations was amplified on Twitter, focusing primarily on US news sources such as Fox News, the New York Times and BuzzFeed.
The study compared Twitter’s “Home” timeline – the default way its 200 million users are served tweets, in which an algorithm tailors what users see – with the traditional chronological timeline where the most recent tweets are ranked first.
The research found that: in six out of seven countries, apart from Germany, tweets from rightwing politicians received more amplification from the algorithm than those from the left; right-leaning news organisations were more amplified than those on the left; and generally, politicians’ tweets were more amplified by an algorithmic timeline than by the chronological timeline.
According to a 27-page research document, Twitter found a “statistically significant difference favouring the political right wing” in all the countries except Germany. Under the research, a value of 0% meant tweets reached the same number of users on the algorithm-tailored timeline as on its chronological counterpart, whereas a value of 100% meant tweets achieved double the reach. On this basis, the most powerful discrepancy between right and left was in Canada (Liberals 43%; Conservatives 167%), followed by the UK (Labour 112%; Conservatives 176%). Even excluding top government officials, the results were similar, the document said.
Twitter said it wasn’t clear why its Home timeline produced these results and indicated that it may now need to change its algorithm. A blog post by Rumman Chowdhury, Twitter’s director of machine-learning ethics, transparency and accountability, and Luca Belli, a Twitter researcher, said the findings could be “problematic” and that more study needed to be done. The post acknowledged that it was concerning if certain tweets received preferential treatment as a result of the way in which users interacted with the algorithm tailoring their timeline.
“Algorithmic amplification is problematic if there is preferential treatment as a function of how the algorithm is constructed versus the interactions people have with it. Further root cause analysis is required in order to determine what, if any, changes are required to reduce adverse impacts by our Home timeline algorithm,” the post said.
Twitter said it would make its research available to outsiders such as academics and is preparing to let third parties have wider access to its data, in a move likely to put further pressure on Facebook to do the same. Facebook is being urged by politicians on both sides of the Atlantic to distribute its research to third parties after tens of thousands of internal documents, which included revelations that the company knew its Instagram app damaged teenage mental health, were leaked by the whistleblower Frances Haugen.
The Twitter study compared the two ways in which a user can view their timeline: the first uses an algorithm to provide a tailored view of tweets; the other displays all tweets by followed accounts, ranked chronologically.
The study compared the two types of timeline by considering whether some politicians, political parties or news outlets were more amplified than others. The study analysed millions of tweets from elected officials between 1 April and 15 August 2020 and hundreds of millions of tweets from news organisations, largely in the US, over the same period.
Twitter said it would make its research available to third parties but said privacy concerns prevented it from making available the “raw data”. The post said: “We are making aggregated datasets available for third party researchers who wish to reproduce our main findings and validate our methodology, upon request.”
Twitter added that it was preparing to make internal data available to external sources on a regular basis. The company said its machine-learning ethics, transparency and accountability team was finalising plans in a way that would protect user privacy.
“This approach is new and hasn’t been used at this scale, but we are optimistic that it will address the privacy-vs-accountability tradeoffs that can hinder algorithmic transparency,” said Twitter. “We’re excited about the opportunities this work may unlock for future collaboration with external researchers looking to reproduce, validate and extend our internal research.”