Gender in Twitter: Styles, stances, and social networks

By David Bamman, Jacob Eisenstein, Tyler Schnoebelen. 2012

54 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.

Citations in the corpus

"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, methodology): “Some approaches used lists of male and female names, for example obtained using Facebook (Fink, Kopecky, and Morawski 2012) or from the US social security department (Zamal, Liu, and Ruths 2012; Bamman, Eisenstein, and Schnoebelen 2012).”
Gender Inference of Twitter Users in Non-English Contexts (2013, background): “differently by men and women online as well (Bamman et al., 2012).”
Predictive Analysis on Twitter: Techniques and Applications (2018, unknown): “Existing approaches utilized statistical features [115] and seldom involved background knowledge along with social information.”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1815–1827, Seattle, Washington, USA, 18-21 October 2013. c©2013 Association for Computational Linguistics”
Key Female Characters in Film Have More to Talk About Besides Men: Automating the Bechdel Test (2015, background): “There has been much work in the computational sciences community on studying gender differences in the way language is used by men versus women (Peersman et al., 2011; Mohammad and Yang, 2011; Bamman et al., 2012; Schwartz et al., 2013; Bam-man et al., 2014; Prabhakaran et al., 2014).”
What to do about bad language on the internet (2013, methodology): “Using a dataset of one million English-language tweets (Bamman et al., 2012), I have computed the average length of messages containing both standard words and their non-standard alternatives, focusing on the top five non-standard shortenings identified by the automatic method of Gouws et al.”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “0% over twitter data [68] while our classifiers reach an accuracy of 91.”