Social media can give early warning of displacement ^{CEFR B2}

Researchers report that analysing social media posts can offer early warnings of population movements, potentially helping humanitarian organisations respond more quickly. The study appears in EPJ Data Science and evaluates whether digital signals can complement or substitute traditional sources when surveys and field data are unavailable.

The research analysed nearly 2 million posts in three languages on X and used three case studies: Ukraine (10.6 million displaced), Sudan (approximately 12.8 million displaced) and Venezuela (about 7 million displaced). The team compared methods for classifying posts and found that sentiment labels (positive, negative, neutral) gave more reliable signals than discrete emotion labels (joy, anger, fear), especially for forecasting the timing and volume of cross‑border movements. Pretrained language models were the most effective early warning tools.

Marahrens explains the approach worked best in rapid conflict settings such as Ukraine and less well in slower economic crises like Venezuela. He cautions that social media analysis can trigger false alarms and is most valuable as an initial alert that prompts deeper investigation, particularly when combined with economic indicators and on‑the‑ground reports. The study also recommends exploring the relation between sentiment and emotion, using automated translation for more languages, and adding data from other networks to strengthen the tools.

The research received funding from the National Science Foundation and Georgetown University’s Massive Data Institute. Source: University of Notre Dame.