A Unified Framework for Understanding and Intervening on False News Sharing
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The risks of false news—its potential to distort public opinion and erode trust—have driven extensive research on potential countermeasures. Yet, there has been no comprehensive, comparative, and computational investigation of false news sharing and how to mitigate it. To address this, we apply a semi-integrative experimental approach that directly compares multiple false news interventions, examines individual- and item-level modifiers of intervention efficacy, and uses computational modeling to uncover the decision-making processes underlying these effects. We find that warning labels and media literacy interventions most effectively improve news sharing quality, followed by a social norm intervention. Accuracy prompts were least effective. Supporting generalizability, intervention effects remained consistent across individual- and item-level characteristics, including age, analytical thinking, and political-lean. Supporting specificity, each intervention influenced news sharing quality through distinct decision-making processes. By applying a semi-integrative approach, we offer an in-depth understanding of false news sharing and how to mitigate it.
