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Lost in Space: Geolocation in Event Data

Lee, Sophie J and Liu, Howard and Ward, Michael D (2019) 'Lost in Space: Geolocation in Event Data.' Political Science Research and Methods, 7 (04). pp. 871-888. ISSN 2049-8470

1611.04837.pdf - Accepted Version

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Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Item Type: Article
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Government, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 02 Sep 2021 09:41
Last Modified: 06 Jan 2022 14:19

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