Climate change (interlinked with humanitarian crises and other economic and health factors) could lead to internal resettlements, international migration, and other (new) forms of human mobility. However, the empirical link between various climatic conditions and migration outcomes is highly contested, and, to date, no unified theoretical approach can adequately capture the complexity and contextual dependency of climate-induced migration. To address this gap, CLIMB seeks to develop a holistic approach which will allow us to better understand the mechanisms and pathways underlying the climate-migration nexus, and to predict temporal-spatial mobility patterns in Africa and beyond. To achieve our goal, CLIMB will leverage high-resolution and high-frequency Big Data, including Call Detailed Records (CDR), satellite imageries, and digital traces on social media. The resources requested on Alvis will be specifically devoted to processing digital trace data on X (formerly Twitter) including one-billion tweets. We will use this rich dataset to derive high-dimensional indicators which aim to capture 1) population’s emotions and fears about environmental hazards and about geo-political and economic uncertainties, 2) behavioral responses (including intentions to migrate, where to migrate); 3) mapping migrants’ transnational networks, as well as host communities’ attitudes and mood toward migrant and displaced populations.