TY - GEN
T1 - RtGender
T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018
AU - Voigt, Rob
AU - Jurgens, David
AU - Prabhakaran, Vinodkumar
AU - Jurafsky, Dan
AU - Tsvetkov, Yulia
N1 - Funding Information:
This work was supported by the Stanford Data Science Initiative and the National Science Foundation through award IIS-1526745. The first author gratefully acknowledges the support of the Stanford Interdisciplinary Graduate Fellowship.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Like many social variables, gender pervasively influences how people communicate with one another. However, prior computational work has largely focused on linguistic gender difference and communications about gender, rather than communications directed to people of that gender, in part due to lack of data. Here, we fill a critical need by introducing a multi-genre corpus of more than 25M comments from five socially and topically diverse sources tagged for the gender of the addressee. Using these data, we describe pilot studies on how differential responses to gender can be measured and analyzed and present 30k annotations for the sentiment and relevance of these responses, showing that across our datasets responses to women are more likely to be emotive and about the speaker as an individual (rather than about the content being responded to). Our dataset enables studying socially important questions like gender bias, and has potential uses for downstream applications such as dialogue systems, gender detection or obfuscation, and debiasing language generation.
AB - Like many social variables, gender pervasively influences how people communicate with one another. However, prior computational work has largely focused on linguistic gender difference and communications about gender, rather than communications directed to people of that gender, in part due to lack of data. Here, we fill a critical need by introducing a multi-genre corpus of more than 25M comments from five socially and topically diverse sources tagged for the gender of the addressee. Using these data, we describe pilot studies on how differential responses to gender can be measured and analyzed and present 30k annotations for the sentiment and relevance of these responses, showing that across our datasets responses to women are more likely to be emotive and about the speaker as an individual (rather than about the content being responded to). Our dataset enables studying socially important questions like gender bias, and has potential uses for downstream applications such as dialogue systems, gender detection or obfuscation, and debiasing language generation.
KW - Computational social science
KW - Discourse
KW - Gender bias
KW - Gender difference
KW - Gender-annotated corpora
UR - http://www.scopus.com/inward/record.url?scp=85059893439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059893439&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059893439
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 2814
EP - 2820
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
Y2 - 7 May 2018 through 12 May 2018
ER -