Using Noisy Self-Reports to Predict Twitter User Demographics

Zach Wood-Doughty, Paiheng Xu, Xiao Liu, Mark Dredze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 prole descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.

Original languageEnglish (US)
Title of host publicationSocialNLP 2021 - 9th International Workshop on Natural Language Processing for Social Media, Proceedings of the Workshop
EditorsLun-Wei Ku, Cheng-Te Li
PublisherAssociation for Computational Linguistics (ACL)
Pages123-137
Number of pages15
ISBN (Electronic)9781954085329
StatePublished - 2021
Event9th International Workshop on Natural Language Processing for Social Media, SocialNLP 2021 - Virtual, Online
Duration: Jun 10 2021 → …

Publication series

NameSocialNLP 2021 - 9th International Workshop on Natural Language Processing for Social Media, Proceedings of the Workshop

Conference

Conference9th International Workshop on Natural Language Processing for Social Media, SocialNLP 2021
CityVirtual, Online
Period6/10/21 → …

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Software

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