Discriminating Gender on Twitter

By John D. Burger, John C. Henderson, George Kim, Guido Zarrella. 2011

651 citations recorded by the source corpus.

Subfields: Natural Language Processing, Information Extraction

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In Natural Language Processing

In Information Extraction

Citations in the corpus

Women also Snowboard: Overcoming Bias in Captioning Models (2018, background): “Gender classification has also been studied in natural language processing ([41,42], [43]).”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, methodology): “Burger et al. (2011) sampled users from the Twitter stream and used links to blogging sites, indicated in their proﬁle, to ﬁnd the gender.”
Adversarial Removal of Demographic Attributes from Text Data (2018, background): “…well established that textual information can be predictive of age, race, gender, and many other social factors of the author (Kop-pel et al., 2002; Burger et al., 2011; Nguyen et al., 2013; Weren et al., 2014; Verhoeven and Daele-mans, 2014; Rangel et al., 2016; Verhoeven et al., 2016; Blodgett…”
Gender as a Variable in Natural-Language Processing: Ethical Considerations (2017, background): “Burger et al. (2011) assigned gender to Twitter users by following links from Twitter accounts to users’ blogs on blogging platforms that required users to indicate their genders.”
User Demographics and Language in an Implicit Social Network (2012, methodology): “Burger et al. (2011) train a gender classiﬁer on tweets with word and character-based ngram features achieving accuracy of 75.5%.”
Attributed Social Network Embedding (2017, background): “Many studies have justified their importance, ranging from user demographics [7], to subjective preference like political orientation and personal interests [8].”
ConStance: Modeling Annotation Contexts to Improve Stance Classification (2017, background): “Still, researchers often decide what context to show annotators without rigorously evaluating how their decisions will affect annotations, on tasks from gender identiﬁcation to political leanings (Chen et al., 2015; Nguyen et al., 2014; Burger et al., 2011; Cohen and Ruths, 2013).”
Learning Multimodal Gender Profile using Neural Networks (2017, background): “Burger et al. (2011) is one of the ﬁrst works about gender identiﬁcation in Twitter.”
Developing Age and Gender Predictive Lexica over Social Media (2014, methodology): “Because Twitter does not make gender or age available, such work infers gender and age by leveraging proﬁle information, such as gender-discriminating names or crawling for links to publicly available data (e.g. Burger et al., 2011).”
Semi-supervised Gender Classification with Joint Textual and Social Modeling (2016, background): “More recently, Burger et al. (2011) mention the importance of using unlabeled data and directly apply a self-training approach to perform semi-supervised learning for gender classification.”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “often by females [34,36,38], but Huffaker & Calvert found males use them more in a sample of 100 teenage bloggers [39].”
Understanding User Profiles on Social Media for Fake News Detection (2018, background): “exploit various text-based features from common explicit profile attributes and build SVM to build gender predictor [11]; Liu et al.”
Social and Emotional Correlates of Capitalization on Twitter (2018, methodology): “For the purpose of training a gender classifier Burger et al. (2011) built a corpus of approximately 213 million tweets from 18.5 million users and annotated them for gender by following links to users’ Facebook or MySpace profiles, where self reporting of gender was required.”
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).”
Extracting Human Temporal Orientation from Facebook Language (2015, background): “Toward attribute prediction, a large proportion of works have focused on demographics (Argamon et al., 2009; Goswami et al., 2009; Burger et al., 2011; Al Zamal et al., 2012; Bergsma et al., 2013; Sap et al., 2014). and personality prediction (Mairesse et al., 2007; Iacobelli et al., 2011; Schwartz…”
The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum (2017, background): “[5], more data about a group in prediction tasks generally improves accuracy of the algorithm.”
Demographics of News Sharing in the U.S. Twittersphere (2017, background): “graphics vary across different U.S states). After that, several efforts have arisen that investigate demographic information, in various social media, using different strategies for distinct purposes [6,22,8]. Particularly, researchers are jointly applying computer science and statistical techniques”