Social Media Data for Population Mapping: A Bayesian Approach to Address Representativeness and Privacy Challenges

TL;DR

This paper develops a Bayesian model to improve population estimates from social media data, addressing privacy-induced biases and enabling dynamic, reliable population monitoring for disaster response.

Contribution

It introduces a Bayesian imputation and modeling framework that corrects for privacy biases and links social media data to true populations, enhancing real-time demographic monitoring.

Findings

Bayesian imputation recovers missing data in rural areas.

Model achieves 18-24% error in population proportion estimates.

Accounting for overdispersion and spatial correlation improves accuracy.

Abstract

Accurate and timely population data are essential for disaster response and humanitarian planning, but traditional censuses often cannot capture rapid demographic changes. Social media data offer a promising alternative for dynamic population monitoring, but their representativeness remains poorly understood and stringent privacy requirements limit their reliability. Here, we address these limitations in the context of the Philippines by calibrating Facebook user counts with the country's 2020 census figures. First, we find that differential privacy techniques commonly applied to social media-based population datasets disproportionately mask low-population areas. To address this, we propose a Bayesian imputation approach to recover missing values, restoring data coverage for $5.5\%$ of rural areas. Further, using the imputed social media data and leveraging predictors such as urbanisation level, demographic composition, and socio-economic status, we develop a statistical model for the proportion of Facebook users in each municipality, which links observed Facebook user numbers to the true population levels. Out-of-sample validation demonstrates strong result generalisability, with errors as low as ${\approx}18\%$ and ${\approx}24\%$ for urban and rural Facebook user proportions, respectively. We further demonstrate that accounting for overdispersion and spatial correlations in the data is crucial to obtain accurate estimates and appropriate credible intervals. Crucially, as predictors change over time, the models can be used to regularly update the population predictions, providing a dynamic complement to census-based estimates. These results have direct implications for humanitarian response in disaster-prone regions and offer a general framework for using biased social media signals to generate reliable and timely population data.