用户级聚合对基于词汇的人口级预测的显著好处
The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions
摘要：
基于社交媒体文本的即时预报有望提供对社区层面结果的不引人注目和近乎实时的预测。这些结果通常与人有关，但数据通常是在不考虑每个社区的Twitter用户的情况下汇总的。本文描述了一种简单而有效的方法来建立社区级模型，使用Twitter语言聚集的用户。四个不同的美国县级任务的结果，跨越人口统计学，健康和心理结果，显示出预测准确性的大幅度和一致性的改善（例如，从皮尔逊r= 0.73到0.82的中位数收入预测或r= 0.37到0.47的生活满意度预测）超过了聚合所有推文的标准方法。我们将来自370亿条推文的汇总和匿名社区级数据-其中超过10亿条被映射到县，可用于研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
