Using Noisy Self-Reports to Predict Twitter User Demographics

• URL: http://arxiv.org/abs/2005.00635v2
• Date: Sun, 11 Jul 2021 08:02:36 GMT
• Title: Using Noisy Self-Reports to Predict Twitter User Demographics
• Authors: Zach Wood-Doughty, Paiheng Xu, Xiao Liu, Mark Dredze
• Abstract summary: 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.
• Score: 17.288865276460527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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