Twitter amplifies tweets from right-leaning political parties and news outlets more than from the left, its own research suggests.
According to BBC, the social-media giant said it made the discovery while exploring how its algorithm recommends political content to users. But it admitted it did not know why, saying that was a “more difficult question to answer”. Twitter’s study examined tweets from political parties and users sharing content from news outlets in seven countries around the world: Canada, France, Germany, Japan, Spain, the UK, and the US.
It analysed millions of tweets sent between 1 April and 15 August 2020.
According to The Guardian, 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.
According to a 27-page research document, Twitter found a “statistically significant difference favouring the political right wing” in all the countries except Germany.
They found that mainstream parties and outlets on the political right enjoyed higher levels of “algorithmic amplification” compared with their counterparts on the left, according to a report by BBC.
A blog post by Rumman Chowdhury, director of Twitter’s Meta (machine-learning, ethics, transparency, and accountability) team, said the company’s next step was to find out the reason behind the phenomenon.
“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. 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,” she 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 post said. “Establishing why these observed patterns occur is a significantly more difficult question to answer and something Meta will examine.”
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