Incident 296: Twitter Recommender System Amplified Right-Leaning Tweets

Description: Twitter’s “Home” timeline algorithm was revealed by its internal researchers to have amplified tweets and news of rightwing politicians and organizations more than leftwing ones in six out of seven studied countries.

Tools

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Incident Stats

Incident ID
296
Report Count
3
Incident Date
2016-02-10
Editors
Khoa Lam
Applied Taxonomies
GMF, MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

1.3. Unequal performance across groups

Risk Domain

  1. Discrimination and Toxicity

Entity

AI

Timing

Post-deployment

Intent

Unintentional

Twitter’s algorithm does not seem to silence conservatives
economist.com · 2020

SINCE LAUNCHING a policy on “misleading information” in May, Twitter has clashed with President Donald Trump. When he described mail-in ballots as “substantially fraudulent”, the platform told users to “get the facts” and linked to articles…

Twitter admits bias in algorithm for rightwing politicians and news outlets
theguardian.com · 2021

Twitter has admitted it amplifies more tweets from rightwing politicians and news outlets than content from leftwing sources.

The social media platform examined tweets from elected officials in seven countries – the UK, US, Canada, France, …

Algorithmic amplification of politics on Twitter
pnas.org · 2021

Significance

The role of social media in political discourse has been the topic of intense scholarly and public debate. Politicians and commentators from all sides allege that Twitter’s algorithms amplify their opponents’ voices, or silence…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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