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

Autor:	Giorgi, Salvatore, Preotiuc-Pietro, Daniel, Buffone, Anneke, Rieman, Daniel, Ungar, Lyle H., Schwartz, H. Andrew
Rok vydání:	2018
Předmět:	Computer Science - Social and Information Networks Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in the proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1808.09600 Zobrazit plný text záznamu View this record from Arxiv