Gender and Power: How Gender and Gender Environment Affect Manifestations of Power

By Vinodkumar Prabhakaran, Emily E. Reid, Owen Rambow. 2014

35 citations recorded by the source corpus.

We investigate the interaction of power, gender, and language use in the Enron email corpus. We present a freely available extension to the Enron corpus, with the gender of senders of 87% messages reliably identified. Using this data, we test two specific hypotheses drawn from the sociolinguistic literature pertaining to gender and power: women managers use face-saving communicative strategies, and women use language more explicitly than men to create and maintain social relations. We introduce the notion of “gender environment” to the computational study of written conversations; we interpret this notion as the gender makeup of an email thread, and show that some manifestations of power differ significantly between gender environments. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions.

Subfields: Language and Linguistics, Literature and Literary Theory, Human-Computer Interaction

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Citations in the corpus

Politeness Transfer: A Tag and Generate Approach (2020, background): “Prior work on Enron corpus (Yeh and Harnly, 2006) has been mostly from a socio-linguistic perspective to observe social power dynamics (Bramsen et al., 2011; McCallum et al., 2007), formality (Pe-terson et al., 2011) and politeness (Prabhakaran et al., 2014).”
Connotation Frames of Power and Agency in Modern Films (2017, background): “There is much prior research focus on bias in social media (Garcia et al., 2014; Prabhakaran et al., 2014; Ratkiewicz et al., 2011; Yano et al., 2010; Srivastava and Sahami, 2009), complementing our investigation on movies.”
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; Bamman et al., 2014; Prabhakaran et al., 2014).”
Can Large Language Models Transform Computational Social Science? (2023, background): “…70% may be a sufficient threshold for direct use in downstream analyses.5 In such scenarios, zero-shot prompted models could replace fine-tuned models and thus remove the need for expensive train- 5 For example, Prabhakaran et al. (2014) trained a power relations classifier with 70.74% accuracy.”
Computational Sociolinguistics: A Survey (2016, methodology): “For example, gender information for names can be derived from census information from the US Social Security Administration [17, 212], or from Facebook data [79].”