Drought Displacement Forecasts Can Be Improved With Twitter Data

• Main Authors: Tarraga, Jose Maria, Piles, Maria, Kamateri, Eleni, Marco, Eva Sevillano, Tsampoulatidis, Ioannis, Munoz-Mari, Jordi, Camps-Valls, Gustau
• Format: Conference Proceeding
• Language: English
• Subjects: Blogs; Drought-induced displacement; Droughts; Machine learning; Measurement uncertainty; social media; Social networking (online); Soft sensors; Twitter; Weather forecasting
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10642237

Displacement of human populations due to more extreme weather hazards is a global phenomenon that leads to significant human and economic losses. Mobility related to slow-onset events such as droughts is particularly challenging to model because the start and duration of droughts are uncertain, and their effects are often intertwined with other contextual factors, such as conflicts, political stability, and undependable market prices, that are difficult to measure. Moreover, the collection of in situ socioeconomic data poses a significant challenge, where the use of alternative data sources to warn and plan for impending waves of displacement effectively could help. This study investigates the use of social media as an additional input feature to enhance drought-induced displacement predictions. A methodology for identifying human displacement based on tweet activity is proposed. Results from displacement models based on interpretable machine learning, socioeconomic data, and weather indicators consistently show the benefit of including Twitter data when applied at the district level in Somalia. The proposed approach could enhance drought-induced displacement predictions, thereby helping anticipatory action and the planning of humanitarian interventions.