Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election
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Abstract (arXiv)
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.
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References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2016Bag of Tricks for Efficient Text Classificationcited by 869
- 2021VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twittercited by 7
- 2024Leveraging Contrastive Learning for Few-shot Geolocation of Social Postscited by 2
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