The Impact of Blame Attribution on Moral Contagion in Controversial Events

Controversial events are social incidents that trigger wide discussion and strong emotions, often touching on public interests, moral judgment, or social values. Their diffusion typically involves moral evaluations and affect-laden language. Prior work has mostly examined how the quantity of moral and emotional words shapes diffusion, while largely overlooking blame attribution-that is, whether audiences locate the cause of a controversial event in individual actions or in social structures, across different contexts. Using 189,872 original Weibo posts covering 105 events in three domains- street-level bureaucracy (SLB; individual attribution), education governance (EG; structural attribution), and gender-based violence (GBV; mixed attribution)-we estimate negative binomial models with an interaction between word type and account verification and report incidence rate ratios (IRR). Moral contagion is strongest for SLB (IRR = 1.337) and attenuated for EG (IRR = 1.037). For GBV, moral-emotional language decreases reposts (IRR = 0.844). Unverified accounts amplify the diffusion advantage of moral-emotional wording for both individually and structurally attributed issues, with the largest gains in SLB. When disaggregating by valence and discrete emotions, fear-type moral-emotional words are positively associated with reposts in GBV (IRR = 1.314). Theoretically, we shift the question from whether moral contagion occurs to when it operates, highlighting attribution tendencies and verification status as key moderators. Empirically, we provide cross-issue evidence from large-scale Chinese social media. Methodologically, we offer a replicable workflow that combines length-normalized lexical measures with negative binomial models, including interaction terms.