ITHACA, N.Y. – New Cornell University research finds that both the type of moderator – human or AI – and the “temperature” of harassing content online influenced people’s perception of the moderation decision and the moderation system.
The study used a custom social media site, on which people can post pictures of food and comment on other posts. The site contains a simulation engine, Truman, an open-source platform that mimics other users’ behaviors (likes, comments, posts) through preprogrammed bots created and curated by researchers.
The Truman platform – named after the 1998 film, “The Truman Show” – was developed at the Cornell Social Media Lab led by Natalie Bazarova, professor of communication.
“The Truman platform allows researchers to create a controlled yet realistic social media experience for participants, with social and design versatility to examine a variety of research questions about human behaviors in social media,” Bazarova said. “Truman has been an incredibly useful tool, both for my group and other researchers to develop, implement and test designs and dynamic interventions, while allowing for the collection and observation of people’s behaviors on the site.”
For the study, nearly 400 participants were told they’d be beta testing a new social media platform. They were randomly assigned to one of six experiment conditions, varying both the type of content moderation system (other users; AI; no source identified) and the type of harassment comment they saw (ambiguous or clear).
Participants were asked to log in at least twice a day for two days; they were exposed to a harassment comment, either ambiguous or clear, between two different users (bots) that was moderated by a human, AI or unknown source.
The researchers found that users are generally more likely to question AI moderators, especially how much they can trust their moderation decision and the moderation system’s accountability, but only when content is inherently ambiguous. For a more clearly harassment comment, trust in AI, human or an unknown source of moderation was approximately the same.
“It’s interesting to see that any kind of contextual ambiguity resurfaces inherent biases regarding potential machine errors,” said Marie Ozanne, the study’s first author and assistant professor of food and beverage management.
Ozanne said trust in the moderation decision and perception of system accountability – i.e., whether the system is perceived to act in the best interest of all users – are both subjective judgments, and “when there is doubt, an AI seems to be questioned more than a human or an unknown moderation source.”
The researchers suggest that future work should look at how social media users would react if they saw humans and AI moderators working together, with machines able to handle large amounts of data and humans able to parse comments and detect subtleties in language.
“Even if AI could effectively moderate content,” they wrote, “there is a [need for] human moderators as rules in community are constantly changing, and cultural contexts differ.”
For more information, see this Cornell Chronicle story.
Big Data & Society