Prior research on Twitter (now X) data has provided positive evidence of its
utility in developing supplementary health surveillance systems. In this study,
we present a new framework to surveil public health, focusing on mental health
(MH) outcomes. We hypothesize that locally posted tweets are indicative of
local MH outcomes and collect tweets posted from 765 neighborhoods (census
block groups) in the USA. We pair these tweets from each neighborhood with the
corresponding MH outcome reported by the Center for Disease Control (CDC) to
create a benchmark dataset, LocalTweets. With LocalTweets, we present the first
population-level evaluation task for Twitter-based MH surveillance systems. We
then develop an efficient and effective method, LocalHealth, for predicting MH
outcomes based on LocalTweets. When used with GPT3.5, LocalHealth achieves the
highest F1-score and accuracy of 0.7429 and 79.78\%, respectively, a 59\%
improvement in F1-score over the GPT3.5 in zero-shot setting. We also utilize
LocalHealth to extrapolate CDC's estimates to proxy unreported neighborhoods,
achieving an F1-score of 0.7291. Our work suggests that Twitter data can be
effectively leveraged to simulate neighborhood-level MH outcomes.

利用推特数据进行街区层面的心理健康预测，提出了 LocalTweets 和 LocalHealth 框架，使用 GPT3.5 模型在 MH 监测任务中取得了最高的 F1 得分和准确率，并利用 LocalHealth 方法推测未报告街区的心理健康状况。