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Jha and Mamidi Sexism on Twitter Dataset

Dataset of sexist tweets sampling based on benevolent sexist key phrases from which 712 tweets were manually selected by the authors, and validated by three non-activist feminists. Annotator agreement is Kappa = 0.74.

Data and Resources

Additional Info

Field Value
Paper Authors Jha, A. and Mamidi, R.
Author contact email Jha, A. and Mamidi, R.
Publication / paper reference Jha, A. and Mamidi, R., 2017. When does a Compliment become Sexist? Analysis and Classification of Ambivalent Sexism using Twitter Data. In: Proceedings of the Second Workshop on Natural Language Processing and Computational Social Science. Vancouver, Canada: Association for Computational Linguistics, pp.7-16.
Publication / paper link https://pdfs.semanticscholar.org/225f/f8a6a562bbb64b22cebfcd3288c6b930d1ef.pdf
Publication Year
Dataset about page https://github.com/AkshitaJha/NLP_CSS_2017
Approved
Language(s) covered English
Source data platform(s) Twitter
Phenomena annotated Group-directed Sexism
Level of instances Single comment / post
Data statement link
Total number of instances in dataset 712 new tweets in the 'Benevolent' category - otherwise, combined with Wassem and Hovy's prior datasets
Proportion of positive/abusive instances 1.00
Submitter Laila Sprejer
Submitter Email sprejerlaila@gmail.com
State active