Computational social science studies often contextualize content analysis
within standard demographics. Since demographics are unavailable on many social
media platforms (e.g. Twitter) numerous studies have inferred demographics
automatically. Despite many studies presenting proof of concept inference of
race and ethnicity, training of practical systems remains elusive since there
are few annotated datasets. Existing datasets are small, inaccurate, or fail to
cover the four most common racial and ethnic groups in the United States. We
present a method to identify self-reports of race and ethnicity from Twitter
profile descriptions. Despite errors inherent in automated supervision, we
produce models with good performance when measured on gold standard self-report
survey data. The result is a reproducible method for creating large-scale
training resources for race and ethnicity.

本文提出了一种基于识别 Twitter 个人资料描述自我报告种族和族裔的方法，以解决目前原有数据集较小、不准确或未覆盖美国四个最常见的种族和族裔群体的问题，有效提供了一个可复现的大规模培训种族和族裔判别系统的资源。