Geocoding Without Geotags: A Text-based Approach for reddit
This addresses the challenge of demographic inference for researchers and analysts on pseudonymous social media platforms, though it is incremental as it adapts existing geolocation methods to a new domain.
The paper tackled the problem of geolocation inference on reddit, where pseudonymity hinders supervised methods, by developing a text-based heuristic to generate ground truth labels and showing that domain-specific training outperforms cross-domain transfer, with reddit models achieving improved accuracy using platform metadata.
In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platform-specific interest-group metadata can be used to improve inferences.