@article{Guilbeault2025, 
author = {Douglas Guilbeault and Damon Centola},
title = {Structural Synchronization in Classification of Controversial Content},
year = {2025},
journal = {Journal of Social Computing},
volume = {6},
number = {4},
pages = {287-299},
keywords = {classification, coordination, social networks, online experiment, content moderation, collective intelligence, crowdsourcing},
url = {https://www.sciopen.com/article/10.23919/JSC.2025.0024},
doi = {10.23919/JSC.2025.0024},
abstract = {Morally controversial content, such as offensive and hateful images over social media, is especially challenging to categorize, given widespread disagreement in how people interpret and evaluate this content. Numerous studies argue that a range of subjective biases, such as partisan differences in moral reasoning, lead people not only to diverge in their classifications of controversial content, but also to resist any attempts to change their classification judgments via social influence. 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. This poses a fundamental puzzle: how can populations of diverse individuals who disagree in the interpretation of controversial content nevertheless arrive at highly similar decisions for the classification and removal of such content? Here, we use an online platform to test the hypothesis that structural symmetries in information exchange networks can synchronize convergence on decisions regarding the classification and removal of controversial images across independent networks, leading them to independently reproduce consistent systems of classification. We find that isolated individuals diverge considerably in their classification of controversial content, whereas separate, structurally similar networks independently synchronize in their classifications and content removal decisions, reducing partisan biases across all networks. We also find that when participant experience is compared to subjects evaluating content individually in the control condition, participants within synchronizing networks reported having significantly more positive feelings about their task, and experience significantly less emotional stress when evaluating controversial content.}
}