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Tipsheet

A Twitter Internal Study Claims Platform Amplifies Right-Wing Content

AP Photo/Alexander Zemlianichenko

A study conducted by Twitter discovered that the platform's algorithm tends to recommend posts from right-leaning lawmakers and media outlets more than that of left-wing users.

Twitter began offering users the ability to choose between an algorithmic timeline or a chronological one back in 2016.

"The purpose of this study was to better understand the amplification of elected officials’ political content on our algorithmically ranked Home timeline versus the reverse chronological Home timeline," Rumman Chowdhury, the head of Twitter's machine learning, ethics, transparency and accountability team, and Luca Belli, a Twitter researcher, wrote in a blog post. "We hope our findings will contribute to an evidence-based discussion of the role these algorithms play in shaping political content consumption on the internet."

Millions of tweets from elected officials in seven countries — the United Kingdom, U.S., Canada, France, Germany, Spain and Japan — and hundreds of millions of tweets from news outlets were analyzed by the tech giant between April 1 and Aug. 15 of 2020.

Twitter said it had used third-party sources to determine the political ideology of both politicians and news sources.

Of the seven countries, Germany was the only one where right-wing content was not boosted more than left-wing content.

The study also found that right-leaning content in the U.S. outperformed their left-leaning counterparts.

The platform did not address why the disparities exist but did say that it plans to tackle the issue in the future.

"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 wrote in the blog post.

"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," the blog post continued. "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 platform is offering to share the raw data used for the study with third-party researchers if it is requested.

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