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User-Driven Fact-Checking Behavior, Motivational Barriers and Expertise Asymmetry in Health Communication: A
Elena Artemenko1, Olessia Koltsova1, Maksim Terpilovskii1
1Social & Cognitive Informatics Lab., HSE University, Saint-Petersburg, Russia.
Abstract:
Health misinformation undermines public well-being, yet most research conceptualizes fact-checking as an external intervention targeting false messages only. This obscures the effects of counter-messages on initially truthful statements and diverts attention from fact-checking as a user-driven practice. This study advances misinformation research by examining fact-checking as a spontaneous, naturalistic user behavior, captured via indirect behavioral indicators of external verification, and by testing its predictors and consequences for accuracy in a large-scale online experiment (N = 1,843). Participants evaluated the veracity of health-related statements and could verify information externally; in one condition, an accuracy-based financial incentive was introduced to model motivational stakes. Using Bayesian structural equation modeling, we show that greater fact-checking effort is associated with higher classification accuracy. However, its effectiveness varies by expertise: media professionals benefited more from verification than laypeople, whereas effects for medical professionals were inconsistent. These findings challenge assumptions about domain expertise as a universal safeguard against misinformation and point to the importance of transferable verification routines. Importantly, motivational incentives did not increase either fact-checking effort or accuracy, suggesting that motivational engagement in online verification may be difficult to induce, thereby limiting the feasibility of widespread routine fact-checking by users. Overall, resilience to misinformation appears to depend on the interaction between user-driven verification practices and forms of expertise. Interventions may therefore benefit from combining verification skill training with context-sensitive cues that help users identify high-risk informational situations worthy of fact-checking, rather than relying solely on domain knowledge or external fact-checking institutions.
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