Hierarchical Bayesian Models for Latent Attribute Detection in Social Media


  • Delip Rao Johns Hopkins University
  • Michael Paul Johns Hopkins University
  • Clay Fink Johns Hopkins University
  • David Yarowsky Johns Hopkins University
  • Timothy Oates University of Maryland Baltimore County
  • Glen Coppersmith JHU Human Language Technology Center of Excellence


We present several novel minimally-supervised models for detecting latent attributes of social media users, with a focus on ethnicity and gender. Previouswork on ethnicity detection has used coarse-grained widely separated classes of ethnicity and assumed the existence of large amounts of training data such as the US census, simplifying the problem. Instead, we examine content generated by users in addition to name morpho-phonemics to detect ethnicity and gender. Further, weaddress this problem in a challenging setting where the ethnicity classes are more fine grained -- ethnicity classes in Nigeria -- and with very limited training data.




How to Cite

Rao, D., Paul, M., Fink, C., Yarowsky, D., Oates, T., & Coppersmith, G. (2021). Hierarchical Bayesian Models for Latent Attribute Detection in Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 598-601. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14197