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

2026-01-29

AI Summary

This study addresses the limitations of traditional censuses in capturing rapid demographic changes and the representativeness bias in social media data, particularly the undercoverage of rural areas due to differential privacy mechanisms. To overcome these challenges, the authors propose a Bayesian inference framework that integrates the 2020 Philippine census with urbanization levels, demographic structure, and socioeconomic indicators to dynamically estimate true population distributions. The approach innovatively employs Bayesian imputation to recover rural population data obscured by differential privacy, while explicitly modeling spatial correlation and overdispersion to enable temporally updated population forecasts. Out-of-sample validation demonstrates that the method successfully recovers coverage for 5.5% of previously missing rural areas and reduces prediction errors in urban and rural Facebook user ratios to approximately 18% and 24%, respectively, thereby significantly enhancing the timeliness and accuracy of population estimates for disaster response.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Privacy
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• population mapping
• social media data
• representativeness
• privacy
• humanitarian planning

Innovation

• Bayesian imputation
• differential privacy
• population mapping
• social media data
• spatial correlation