Examining social and cultural questions using computational text analysis carries significant challenges. Texts can be socially and culturally situated. They reflect ideas of both, authors and their target audiences. Such interpretation is hard to incorporate in computational approaches.

Research questions should be identified through data selection, conceptualization and operationalization, and end with analysis and interpretation of results.

The person who produces sexist language is not given any space for productive change, but may simply become more entrenched in their position (Post-feminist analysis, 236)

Attempt reforming sexism in language can become a failure if it simply focuses on the eradication of certain phrases and words.

Because of this, approaching digital text collection from a literary textual critic’s perspective might require questioning the context behind the digital text. Rather than working on raw text and relying on results produced by machine processing, it will make more sense to understand the environment, reason and validity of the information provided in the text.

Instead of taking data at face value and looking toward future insights, data scientists can first interrogate the context, limitations and validity of the data under use. This being said, feminist strategy for considering context is to consider the cooking process that produces “raw” data (Klein and D’lgnazio, Numbers don’t speak for themselves, 14)

Researches too easily attribute phraseological differences to gender when in fact other intersecting variables might be at play. As far as gender can be counted as a performative language use in its social context, it is important to avoid dataset and interpretational bias. (These Are Not The stereotypes You are looking for”,15)

• D’Ignazio, Catherine;  Klein, Lauren; –  Data Feminism. 3. On Rational, Scientic, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints 6. The Numbers Don’t Speak for Themselves. Published on: Mar 16, 2020
• Dong Nguyen, Maria Liakata, Simon DeDeo, Jacob Eisenstein, David Mimno, Rebekah Tromble, and Jane Winters. “How We Do Things With Words: Analyzing text as Social and Cultural Data”. Published online 2020 Aug 25
• Koolen, C., & van Cranenburgh, A. (2017). These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution. In Proceedings of the First Ethics in NLP workshop (pp. 12-22)