The spreading of misinformation via social media contributes to a global threat to trust in science and democratic institutions, with consequences for public health and societal conflicts. The fact that emotion biases information processing suggests a link between certain emotional states and misinformation spreading, which becomes visible especially in situations of high uncertainty. This project aims at understanding how emotions influence the tendency to believe and share inaccurate content, and to test intervention strategies to mitigate emotional misinformation spreading. We combine three approaches: (1) characterizing the dynamics of emotional misinformation spreading using large-scale social media data, (2) experimentally testing the potential of individual emotion regulation interventions to reduce misinformation sharing, and (3) integrating both sources of evidence to inform an agent-based model. This allows identifying the most promising interventions to reduce misinformation spreading at the macro scale, and simulating how algorithmic filters for emotional information affect the spreading of misinformation through social networks. Together, these results will provide guidance on how to analyze and adapt information and communication technologies taking into account human ways of information processing. This project approaches the problem of misinformation spreading from a radically new angle, directly tackling emotional influences on attention and sharing behavior.