Hierarchical Bayesian Models for Latent Attribute Detection in Social Media

By Delip Rao, Michael J. Paul, Clayton Fink, David Yarowsky, Timothy Oates, Glen A. Coppersmith. 2011

89 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “…attract interest by researchers as a resource to study automatic identiﬁcation of user attributes, such as ethnicity (Pennacchiotti and Popescu 2011; Rao et al. 2011), gender (Fink, Kopecky, and Morawski 2012; Bamman, Eisenstein, and Schnoebelen 2012; Rao et al. 2010; Burger et al. 2011; Rao et…”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, methodology): “27:18 N. Z. Gong et al. Previous works [Rao et al. 2011, 2010] aim at inferring node attributes (e.g., ethnicity and political orientation) using supervised learning methods with features extracted from user names and user-generated texts.”
Computational Sociolinguistics: A Survey (2016, background): “Vyas et al. (2014) studied the impact of different preprocessing steps on POS tagging of English-Hindi data collected from Facebook.”
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses (2015, background): “Since Twitter does not provide demographic information for users, these insights have been broadly applied to inferring demographic information from social media (Volkova et al., 2015; Fink et al., 2012; Burger et al., 2011; Rao et al., 2011; Rao et al., 2010).”
Stylometric Analysis of Scientific Articles (2012, background): “There has been growing interest in applying stylometry to the content generated by users of Internet applications, e.g., detecting author ethnicity in social media (Eisenstein et al., 2011; Rao et”