Identification of Fine-Grained Location Mentions in Crisis Tweets
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Abstract (arXiv)
Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 11 Nov 2021.
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15 of 46 references.
References · 15
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3 more
- 2020A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19cited by 12
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