Towards a Mechanistic Understanding of False News Sharing: Which Interventions Work Best, for Whom, and Why

False news—given its capacity to distort public opinion and erode trust—has prompted extensive research on potential countermeasures. Yet, there has been no systematic, comparative, and computational investigation of false news sharing and how best to curb it. To address this gap, we apply a semi-integrative experimental approach that: (1) compares multiple existing false news interventions, (2) examines how individual and news-level factors predict false news sharing and shape intervention efficacy, and (3) uses drift diffusion modeling to uncover the decision-making processes underlying all these effects. We find warning labels and media literacy tips to substantially improve news sharing quality, whereas social norm cues exert a comparatively modest effect, and accuracy prompts yield only subtle benefits. Although numerous individual factors (e.g., age, political conservatism, social media use) predicted news sharing quality, the observed intervention effects remained broadly robust across these factors, proving effective even within at-risk populations. Intervention outcomes were likewise robust to news-level variation, such as the believability, sensationalism, and political congruence of news content. Despite this robustness, we find each intervention to operate via distinct decision-making pathways. Warning labels shift initial sharing intentions towards sharing higher-quality news, whereas media literacy tips operate later, enhancing the processing of news content and increasing cautiousness before making sharing decisions. By applying a multi-component experimental framework, this work clarifies the risk factors and decision-making processes driving false news sharing and pinpoints which interventions work best, how they operate at the process-level, and in which contexts they should be most effective.