Facebook study considered the political leanings of news posts by US users before determining which posts were reached via the site’s social algorithms

The algorithms used by Facebook to filter news posts have an effect on the information seen by users – but not nearly as much as the choices made by users themselves.

That is the finding of a study published on Thursday titled “Exposure to ideologically diverse news and opinion on Facebook” in Science Express by researchers working for the social network.

Eytan Bakshy and Solomon Messing of Facebook, along with Lada Adamic of the University of Michigan, used anonymised data of 10.1m US Facebook users.

They considered the news that users posted online for friends – noting whether it was liberal or conservative – and then determined what kind of news, posted by the users’ friends, actually reached users via the site’s social algorithms.

The study concluded: “Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to [ideologically] cross-cutting content.”

Examining the findings in Science Express, David Lazer, an academic at Harvard and Northeastern universities, said that curation of posts by Facebook did ideologically filter what contents users saw.

He added: “This effect is modest relative to choices people make that filter information, including who their friends are and what they choose to read given the curation. The deliberative sky is not yet falling, but the skies are not completely clear either.”

Lazer describes the finding as important, and one that required “continued vigilance”.

“A small effect today might become a large effect tomorrow, depending on changes in the algorithms and human behaviour. Ironically, these findings suggest that if Facebook incorporated ideology into the features that the algorithms pay attention to, it would improve engagement with content by removing dissonant ideological content.”

While welcoming Facebook’s support of the research, Lazer adds: “There is a broader need for scientists to study these systems in a manner that is independent of the Facebooks of the world. There will be a need at times to speak truth to power, for knowledgeable individuals with appropriate data and analytic skills to act as social critics of this new social order.”

However, the most important lesson of the report by Bakshy et al is “the need to create a new field around the social algorithm, which examines the interplay of social and computational code,” Lazer writes.