Geocoding Without Geotags: A Text-based Approach for reddit

Keith Harrigian


Abstract
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.
Anthology ID:
W18-6103
Volume:
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–27
Language:
URL:
https://aclanthology.org/W18-6103
DOI:
10.18653/v1/W18-6103
Bibkey:
Cite (ACL):
Keith Harrigian. 2018. Geocoding Without Geotags: A Text-based Approach for reddit. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 17–27, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Geocoding Without Geotags: A Text-based Approach for reddit (Harrigian, WNUT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6103.pdf