Twitter Unsure Why its Algorithm Amplifies Right-Leaning Politicians



Twitter posted information about an in-depth analysis of whether its recommendation algorithms amplify political content. The study examined Tweets from elected officials in seven countries: Canada, France, Germany, Japan, Spain, the United Kingdom, and the United States. The study included analysis of millions of Tweets from April 1 to August 15, 2020.

The study examined algorithmic amplification of political content in the Home Timeline (the one that is chronological) by asking the following questions:

  •  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?

Here is what Twitter found:

  •  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.
  •  Group effects did not translate to individual effects. In other words, since party affiliation or ideology is not a factor [Twitter’s] systems consider when recommending content, two individuals in the same party would not necessarily see the same amplification.
  •  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.
  • Right-leaning news outlets, as defined by the independent organizations [All Sides and Ad Fontes Media] see greater algorithmic amplification on Twitter compared to left-leaning news outlets. However, as highlighted in the paper, these third party rankings make their own, independent classifications and as such the results of the analysis may vary depending on which source is used.

In short, they found that Twitter’s algorithms amplifies right-leaning politicians and right-leaning news outlets. It appears Twitter does not know why that is happening. “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,” Twitter stated.

In my opinion, Twitter needs to find out why its algorithms are selecting Tweets from right-leaning politicians and news outlets over Tweets from left-leaning politicians and news outlets. Favoring one side – no matter which side the algorithm chooses – could unfairly influence the result of real-world elections.