The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions

Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, H. Andrew Schwartz


Abstract
Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but the data is often aggregated without regard to users in the Twitter populations of each community. This paper describes a simple yet effective method for building community-level models using Twitter language aggregated by user. Results on four different U.S. county-level tasks, spanning demographic, health, and psychological outcomes show large and consistent improvements in prediction accuracies (e.g. from Pearson r=.73 to .82 for median income prediction or r=.37 to .47 for life satisfaction prediction) over the standard approach of aggregating all tweets. We make our aggregated and anonymized community-level data, derived from 37 billion tweets – over 1 billion of which were mapped to counties, available for research.
Anthology ID:
D18-1148
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1167–1172
Language:
URL:
https://aclanthology.org/D18-1148
DOI:
10.18653/v1/D18-1148
Bibkey:
Cite (ACL):
Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, and H. Andrew Schwartz. 2018. The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1167–1172, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (Giorgi et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1148.pdf
Attachment:
 D18-1148.Attachment.zip