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Predicting Twitter User Demographics from Names Alone.

, , , and . PEOPLES@NAACL-HTL, page 105-111. Association for Computational Linguistics, (2018)

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Using Noisy Self-Reports to Predict Twitter User Demographics., , , and . SocialNLP@NAACL, page 123-137. Association for Computational Linguistics, (2021)DoubleLingo: Causal Estimation with Large Language Models., and . NAACL (Short Papers), page 799-807. Association for Computational Linguistics, (2024)Proxy Model Explanations for Time Series RNNs., , and . ICMLA, page 698-703. IEEE, (2021)How Does Twitter User Behavior Vary Across Demographic Groups?, , , and . NLP+CSS@ACL, page 83-89. Association for Computational Linguistics, (2017)Segment Anything Model is a Good Teacher for Local Feature Learning., , , and . CoRR, (2023)Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status., and . CoRR, (2024)Audio-Journey: Open Domain Latent Diffusion Based Text-To-Audio Generation., , , , , and . ICASSP, page 6960-6964. IEEE, (2024)Model Distillation for Faithful Explanations of Medical Code Predictions., , and . BioNLP@ACL, page 412-425. Association for Computational Linguistics, (2022)Reliability of Topic Modeling., and . CoRR, (2024)Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement., , , , and . Proc. ACM Hum. Comput. Interact., 5 (CSCW1): 107:1-107:28 (2021)