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Twitter logo is displayed on a mobile phone with President Donald Trump's Twitter page shown in the background

In this photo illustration, a Twitter logo is displayed on a mobile phone with President Donald Trump's Twitter page shown in the background on May 27, 2020. (Photo: Olivier Douliery/AFP via Getty Images)

Despite Politicians and Pundits' Claims, Twitter Finds Algorithm Favors Right-Wing Voices

"So much for Trump's claim that Twitter has an anti-conservative bias."

Brett Wilkins

Contrary to claims by former U.S. President Donald Trump and other right-wing politicians and pundits that Twitter favors posts by liberals, new internal research shared Thursday by the social media titan showed that conservative tweets received greater algorithmic amplification on the platform.

"It's honestly amazing that people on the right keep pushing the 'social media is biased against conservatives' line even though every social media platform's algorithm is tilted in their favor."

"So much for Trump's claim that Twitter has an anti-conservative bias. It actually amplifies those voices," tweeted Belinda Barnet, a digital culture and social media expert and professor at Swinburne University of Technology in Melbourne, Australia.

Two days after a pro-Trump mob stormed the U.S. Capitol in January, Twitter permanently suspended the then-president's personal account, @realDonaldTrump, "due to the risk of further incitement of violence."

Kate Starbird, a professor of human-centered design and engineering at the University of Washington, said Friday that "in contrast to the political talking points we often hear at the congressional hearings, Twitter researchers find that their algorithms amplify right-wing influencers and right-leaning [news] sites at much higher rates than content [and] influencers on the left."

Paris Marx, host of the "Tech Won't Save Us" podcast, bluntly remarked that "social media is full of executives who are dumb enough to fall for bullshit claims of anti-conservative bias or are in on it."

Algorithmic amplification refers to the computerized boosting of certain content at the expense of other viewpoints. In an effort to determine how much of an algorithmic boost political content from elected officials receives, and whether such amplification varies among and within political parties, researchers compared the Twitter "home" timelines of users in seven countries—Canada, France, Germany, Japan, Spain, the U.K., and the U.S.—in a review of millions of tweets posted by their elected officials.

"Content on Twitter's home timeline is selected and ordered by personalization algorithms," the paper states. "By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others."

Acknowledging that "there's been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others," the study continues:

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 U.S. media landscape revealed that algorithmic amplification favors right-leaning news sources.

"It's honestly amazing that people on the right keep pushing the 'social media is biased against conservatives' line even though every social media platform's algorithm is tilted in their favor," said author and activist Parker Molloy, who publishes The Present Age, a newsletter covering digital communication issues.

Molloy added she is "glad that Twitter is finally admitting this to some extent, but it's been obvious for years."

Rumman Chowdhury, the head of Twitter's machine learning, ethics, transparency, and accountability team, told Protocol that "we are not entirely sure why [the amplification of right-wing tweets] is happening. To be clear, some of it could be user-driven, people's actions on the platform; we are not sure what it is."

"When algorithms get put out into the world, what happens when people interact with it, we can't model for that," she added. "We can't model for how individuals or groups of people will use Twitter, what will happen in the world in a way that will impact how people use Twitter."

Chowdhury stressed that "the purpose is not to dump the responsibility on users here. There is a lot here for us to think about, how to give people more meaningful choice, more meaningful control over their input [to the algorithms], as well as the output that's going on."

"It's just important that we share this information," she said.

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