Twitter has released a new study that says its algorithm amplifies right-leaning news content and tweets from the political right.

The study was meant to track whether the platform's recommendation algorithms amplify political content at all, according to Twitter Director of Software Engineering Rumman Chowdhury and Staff Machine Learning Researcher Luca Belli. 

The two explained in a blog post that Twitter wanted to "better understand the amplification of elected officials' political content on our algorithmically ranked Home timeline versus the reverse chronological Home timeline."

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They tracked millions of tweets from elected officials in seven countries -- Canada, France, Germany, Japan, Spain, the UK, and the US -- from April 1 to August 15, 2020. They were looking to see whether there was variance within a party, if some types of political groups were algorithmically amplified more than others, and if some news outlets were amplified more by algorithms than others. 

"In six out of seven countries -- all but Germany -- Tweets posted by accounts from the political right receive more algorithmic amplification than the political left when studied as a group. Right-leaning news outlets... see greater algorithmic amplification on Twitter compared to left-leaning news outlets," Chowdhury and Belli said. 

"Tweets about political content from elected officials, regardless of party or whether the party is in power, do see algorithmic amplification when compared to political content on the reverse chronological timeline."

In the US, tweets from Republican Senators and House members were amplified more than Democrats, and the trend continued in almost every other country -- particularly in the UK, where tweets from conservative politicians were amplified significantly more than any other party.  

The study notes that on top of the fact that right-wing news content is amplified, left-leaning news sites see relatively low amplification compared to every other category. Fox News and The New York Post saw more amplification than others. 

The researchers said it's challenging to figure out why this is happening, since the algorithm's actions are a "product of the interactions between people and the platform."

Twitter's ML Ethics, Transparency and Accountability (META) team will now look into the issue and "mitigate any inequity that may occur."

"This research study highlights the complex interplay between an algorithmic system and people using the platform. Algorithmic amplification is not problematic by default -- all algorithms amplify," Chowdhury and Belli said. 

"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 study is part of a larger effort by Twitter to address concerns about the way its algorithms function. 

After significant backlash last year, the company admitted in May that its automatic cropping algorithm repeatedly cropped out Black faces in favor of White ones. It also favored men over women, according to research from Twitter. Multiple Twitter users proved this fact using pictures of themselves or of famous figures, like former President Barack Obama.