Working in groups can help Republicans and Democrats agree on controversial content moderation online
A new study by Annenberg School for Communication professor Damon Centola and alum Douglas Guilbeault explores how content moderators can reach consensus on classifying controversial material online, including inflammatory, offensive, or hateful images.
2 min. read
Over half of Americans believe tech companies should take action to restrict extremely violent content on their platforms, according to Pew data, yet even trained content moderators consistently disagree in their decisions for how to classify hate speech and offensive images. A new study co-authored by Annenberg School for Communication professor Damon Centola has identified a key mechanism to aid content moderators, even those across the political aisle, in reaching consensus on classifying controversial material online: working in teams.
In an experiment involving over 600 participants with diverse political views, Centola and co-author and Penn alum Douglas Guilbeault found that content moderators who classified controversial social media content in groups reached near-perfect agreement on what should remain online. Those who worked alone showed only 38% agreement by the end of the experiment.
“Morally controversial content, such as offensive and hateful images on social media, is especially challenging to categorize, given widespread disagreement in how people interpret and evaluate this content,” Centola says. “Yet, recent large-scale analyses of classification patterns over social media suggest that separate populations, such as Democrats and Republicans, can reach surprising levels of agreement in the categorization of inflammatory content like fake news and hate speech, despite considerable differences in their moral reasoning and worldview. We wanted to know why.”
Centola and Guilbeault had a hunch that a phenomenon called “structural synchronization” might be behind this agreement across partisan divides. “Structural synchronization is a process in which interacting in social networks can filter individual variation and lead separate networks to arrive at highly similar classifications of controversial content,” says Centola.
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