News & Events

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 (a) compares multiple existing false news interventions, (b) examines how individual and news-level factors predict false news sharing and shape intervention efficacy, and (c) 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 toward 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 multicomponent 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.

Advances in AI require a revision of the psychological and socio-technical dynamics by which individuals are radicalized to embrace violent extremism. This review synthesizes process models of radicalization with research on social and personality risk factors, AI, and psychological mechanisms to propose a four-stage framework mapping the AI architecture of radicalization: (1) Exposure, where recommender systems and virality features create initial attraction to extreme content; (2) Reinforcement, where filter bubbles and group recommendations leverage biases to strengthen extremist beliefs and create echo chambers; (3) Group Integration, where ideologically homogenous clusters, AI bot swarms and companions foster group belonging and readiness for action; cumulatively resulting in (4) Violent Extremist Action. We examine how established social, cognitive, personality, and contextual vulnerability factors heighten psychological risk in the AI-driven radicalization process, as well as the emerging role of generative AI. We conclude by outlining a stage-based framework for governance and future research.