Uneven Coverage of Natural Disasters in Wikipedia: the Case of Flood

TL;DR

This study analyzes the coverage of floods in English Wikipedia, revealing biases towards wealthy, English-speaking countries and highlighting disparities in disaster information availability across different income levels.

Contribution

It provides a large-scale automatic content analysis showing geographic and economic biases in Wikipedia's flood coverage, emphasizing implications for disaster response systems.

Findings

Wikipedia coverage is skewed towards wealthy, English-speaking countries.

Flood coverage in low-income countries and South America is substantially lower.

Biases in disaster information can affect emergency detection and response.

Abstract

The usage of non-authoritative data for disaster management presents the opportunity of accessing timely information that might not be available through other means, as well as the challenge of dealing with several layers of biases. Wikipedia, a collaboratively-produced encyclopedia, includes in-depth information about many natural and human-made disasters, and its editors are particularly good at adding information in real-time as a crisis unfolds. In this study, we focus on the English version of Wikipedia, that is by far the most comprehensive version of this encyclopedia. Wikipedia tends to have good coverage of disasters, particularly those having a large number of fatalities. However, we also show that a tendency to cover events in wealthy countries and not cover events in poorer ones permeates Wikipedia as a source for disaster-related information. By performing careful automatic content analysis at a large scale, we show how the coverage of floods in Wikipedia is skewed towards rich, English-speaking countries, in particular the US and Canada. We also note how coverage of floods in countries with the lowest income, as well as countries in South America, is substantially lower than the coverage of floods in middle-income countries. These results have implications for systems using Wikipedia or similar collaborative media platforms as an information source for detecting emergencies or for gathering valuable information for disaster response.