Desaparecidxs: characterizing the population of missing children using Twitter

TL;DR

This paper leverages Twitter data to analyze and characterize the population of missing children in Guatemala, revealing demographic patterns and demonstrating the potential of social media data for societal insights.

Contribution

It introduces a novel approach using Twitter data to study missing children, especially in regions lacking comprehensive official data, providing new insights into demographic patterns.

Findings

Women, especially aged 13-17, are more likely to be reported missing.

Twitter data reveals demographic patterns not captured by official data.

Web data can enhance understanding of hard-to-reach populations.

Abstract

Missing children, i.e., children reported to a relevant authority as having "disappeared," constitute an important but often overlooked population. From a research perspective, missing children constitute a hard-to-reach population about which little is known. This is a particular problem in regions of the Global South that lack robust or centralized data collection systems. In this study, we analyze the composition of the population of missing children in Guatemala, a country with high levels of violence. We contrast the official aggregated-level data from the Guatemalan National Police during the 2018-2020 period with real-time individual-level data on missing children from the official Twitter account of the Alerta Alba-Keneth, a governmental warning system tasked with disseminating information about missing children. Using the Twitter data, we characterize the population of missing children in Guatemala by single-year age, sex, and place of disappearance. Our results show that women are more likely to be reported as missing, particularly those aged 13-17. We discuss the findings in light of the known links between missing people, violence, and human trafficking. Finally, the study highlights the potential of web data to contribute to society by improving our understanding of this and similar hard-to-reach populations.