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    <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.</abstract>
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%0 Conference Proceedings
%T The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions
%A Giorgi, Salvatore
%A Preoţiuc-Pietro, Daniel
%A Buffone, Anneke
%A Rieman, Daniel
%A Ungar, Lyle
%A Schwartz, H. Andrew
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F giorgi-etal-2018-remarkable
%X 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.
%R 10.18653/v1/D18-1148
%U https://aclanthology.org/D18-1148/
%U https://doi.org/10.18653/v1/D18-1148
%P 1167-1172

Markdown (Informal)

[The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions](https://aclanthology.org/D18-1148/) (Giorgi et al., EMNLP 2018)

• The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (Giorgi et al., EMNLP 2018)

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.