User-Level Race and Ethnicity Predictors from Twitter Text

By Daniel Preotiuc-Pietro, Lyle Ungar. 2018

78 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Tanmay Garg
2. Sarah Masud
3. Tharun Suresh
4. Tanmoy Chakraborty
5. Michael J. Paul
6. Diyi Yang
7. Camille Harris
8. Franck Dernoncourt

Citations in the corpus

• Handling Bias in Toxic Speech Detection: A Survey (2022, background): “• Pietro dataset [74]: It is a corpus of 5.”
• Exploring the Role of Grammar and Word Choice in Bias Toward African American English (AAE) in Hate Speech Classification (2022, background): “For example, predicting users’ demographic information [31] to inform other predictions such as offensive speech.”
• Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020, background): “For example, language styles are highly correlated with authors’ demographic attributes, such as age, race, gender and location (Coulmas, 2017; Preoţiuc-Pietro and Ungar, 2018).”