Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for incorporating socio-demographic contexts.

本论文针对社区预测任务提出了一种新的方法：残余因子适应，该方法可有效地整合社区属性，并将语言特征适应到社区属性中。我们使用11个社会经济属性评估了我们的方法，并评估了我们的方法在跨越健康、心理和经济方面的五种不同社区级别预测任务上的效果，结果表明残余因子适应在结合社会人口情况方面显著改善了4个目标预测。

基于残差调整的社区社交媒体预测任务因素适应