In a continuing study on the effects of machine learning (ML) on public conversation, Twitter has confirmed that its algorithms amplify right-leaning political content.
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, – Twitter blogged.
“Right-leaning news outlets, as defined by the independent organizations, see greater algorithmic amplification on Twitter compared to left-leaning news outlets.”
Since 2016, Twitter users are able to choose between viewing algorithmically ordered tweets first in their home timeline or viewing the most recent tweets in reverse chronological order.
An algorithmic home timeline displays a stream of tweets from accounts we have chosen to follow on Twitter, as well as recommendations of other content Twitter thinks we might be interested in based on accounts we interact with frequently, tweets we engage with, and more.
“As a result, what we see on our timeline is a function of how we interact with Twitter’s algorithmic system, as well as how the system is designed.”
The new research is based on tweets of elected officials of House of Commons members in Canada, the French National Assembly, the German Bundestag, House of Representatives in Japan, Congress of Deputies of Spain, House of Commons in the UK, and official and personal accounts of House of Representatives and Senate members in the US, as well as news outlets, from April 1 to August 15, 2020.
The study was conducted by Ferenc Huszar (Twitter, University of Cambridge), Sofia Ira Ktena (now at DeepMind Technologies), Conor O’Brien (Twitter), Luca Belli (Twitter), Andrew Schlaikjer (Twitter), and Moritz Hardt (UC Berkeley).
The questions probed were:
How much algorithmic amplification does political content from elected officials receive in Twitter’s algorithmically ranked Home timeline versus in the reverse-chronological timeline? Does this amplification vary across political parties or within a political party?
Are some types of political groups algorithmically amplified more than others? Are these trends consistent across countries?
Are some news outlets amplified more by algorithms than others? Does news media algorithmic amplification favor one side of the political spectrum more than the other?
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.
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