Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election
This work addresses the need for efficient data processing in social good applications, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of manually annotating crowdsourced citizen reports by introducing Uchaguzi-2022, a dataset of 14k categorized and geotagged reports from the 2022 Kenyan election, and investigated language models for scalable categorization and geotagging.
Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities. Systematically organizing (e.g., categorizing by attributes) and geotagging large amounts of crowdsourced information is crucial to ensuring that accurate and meaningful insights can be drawn from this data and used by policy makers to bring about positive change. These tasks, however, typically require extensive manual annotation efforts. In this paper we present Uchaguzi-2022, a dataset of 14k categorized and geotagged citizen reports related to the 2022 Kenyan General Election containing mentions of election-related issues such as official misconduct, vote count irregularities, and acts of violence. We use this dataset to investigate whether language models can assist in scalably categorizing and geotagging reports, thus highlighting its potential application in the AI for Social Good space.