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In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022, forcibly displaced millions of people from their homes including nearly 6 million refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged social media data from Facebook's advertising platform in combination with preconflict population data to build a real-time monitoring system to estimate subnational population sizes every day disaggregated by age and sex. Using this approach, we estimated that 5.3 million people had been internally displaced away from their baseline administrative region in the first three weeks after the start of the conflict. Results revealed four distinct displacement patterns: large-scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While the use of social media provided one of the only quantitative estimates of internal displacement in the conflict setting in virtual real time, we conclude by acknowledging risks and challenges of these new data streams for the future.

This short data visualisation and accompanying perspective explains recent trends and outlines three reasons to be even more optimistic about the future of Machine Learning in the academic Social Sciences.

Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.