Gender Attribution: Tracing Stylometric Evidence Beyond Topic and Genre

By Ruchi Sarawgi, Kailash Gajulapalli, Yejin Choi. 2011

125 citations recorded by the source corpus.

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Citations in the corpus

Multi-Dimensional Gender Bias Classification (2020, background): “, 2002; Koolen and van Cranenburgh, 2017; Qian, 2019), often driven by the fact that authors of specific genders tend to prefer producing content about topics that belie those gender (Sarawgi et al., 2011).”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, background): “…attributes such as gender and age have been done on a wide range of resources, including telephone conversations (Garera and Yarowsky 2009), blogs (Sarawgi, Gajulapalli, and Choi 2011), forum posts (Nguyen, Smith, and Ros´e 2011) and scientiﬁc articles (Bergsma, Post, and Yarowsky 2012; Sarawgi,…”
Demographic Factors Improve Classification Performance (2015, methodology): “It is often used in author proﬁling (Koppel et al., 2002) and stylometrics (Goswami et al., 2009; Sarawgi et al., 2011).”
Characterizing the Language of Online Communities and its Relation to Community Reception (2016, methodology): “We follow such approaches in our work, acknowledging the challenge of completely separating style/genre and topic factors raised previously (Iyer and Ostendorf, 1999; Sarawgi et al., 2011; Petrenz and Webber, 2011; Axelrod, 2014), which also comes out in our analysis.”
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification (2017, background): “Sarawgi et al. (2011) use long-distance syntactic patterns based on probabilistic context-free grammars, token-level language models and character-level language models.”
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction (2018, background): “Moreover, performance might be overly optimistic due to topic bias (Sarawgi et al., 2011).”
Zooming in on Gender Differences in Social Media (2016, background): “Using a large dataset of over 350 words, we show that gender-based word disambiguation is possible, and that there are indeed differences between the ways certain words are used by men and women.”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, background): “, 2014), gender (Sarawgi et al., 2011; Ciot et al., 2013; Liu and Ruths, 2013; Volkova et al., 2015; Hovy, 2015), personality (Schwartz et al.”
Stylometric Analysis of Scientific Articles (2012, methodology): “While potentially noisy, there is precedent for assigning a single gender to papers “co-authored by researchers of mixed gender” (Sarawgi et al., 2011).”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, unknown): “The list of papers found is here: [12,14–17,26,28,31–35,43–60].”