MISINFORMATIONEmpowering Social Media Users to Assess Content Helps Fight Misinformation
An experimental platform that puts moderation in the hands of its users shows that people do evaluate posts effectively and share their assessments with others.
When fighting the spread of misinformation, social media platforms typically place most users in the passenger seat. Platforms often use machine-learning algorithms or human fact-checkers to flag false or misinforming content for users.
“Just because this is the status quo doesn’t mean it is the correct way or the only way to do it,” says Farnaz Jahanbakhsh, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
She and her collaborators conducted a study in which they put that power into the hands of social media users instead.
They first surveyed people to learn how they avoid or filter misinformation on social media. Using their findings, the researchers developed a prototype platform that enables users to assess the accuracy of content, indicate which users they trust to assess accuracy, and filter posts that appear in their feed based on those assessments.
Through a field study, they found that users were able to effectively assess misinforming posts without receiving any prior training. Moreover, users valued the ability to assess posts and view assessments in a structured way. The researchers also saw that participants used content filters differently — for instance, some blocked all misinforming content while others used filters to seek out such articles.
This work shows that a decentralized approach to moderation can lead to higher content reliability on social media, says Jahanbakhsh. This approach is also more efficient and scalable than centralized moderation schemes, and may appeal to users who mistrust platforms, she adds.
“A lot of research into misinformation assumes that users can’t decide what is true and what is not, and so we have to help them. We didn’t see that at all. We saw that people actually do treat content with scrutiny and they also try to help each other. But these efforts are not currently supported by the platforms,” she says.
Jahanbakhsh wrote the paper with Amy Zhang, assistant professor at the University of Washington Allen School of Computer Science and Engineering; and senior author David Karger, professor of computer science in CSAIL. The research will be presented at the ACM Conference on Computer-Supported Cooperative Work and Social Computing.
