Extracting Associations of Intersectional Identities with Discourse about Institution from Nigeria

Pavan Kantharaju, Sonja Schmer-galunder


Abstract
Word embedding models have been used in prior work to extract associations of intersectional identities within discourse concerning institutions of power, but restricted its focus on narratives of the nineteenth-century U.S. south. This paper leverages this prior work and introduces an initial study on the association of intersected identities with discourse concerning social institutions within social media from Nigeria. Specifically, we use word embedding models trained on tweets from Nigeria and extract associations of intersected social identities with institutions (e.g., domestic, culture, etc.) to provide insight into the alignment of identities with institutions. Our initial experiments indicate that identities at the intersection of gender and economic status groups have significant associations with discourse about the economic, political, and domestic institutions.
Anthology ID:
2022.nlpcss-1.18
Volume:
Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
Month:
November
Year:
2022
Address:
Abu Dhabi, UAE
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–169
Language:
URL:
https://aclanthology.org/2022.nlpcss-1.18
DOI:
Bibkey:
Cite (ACL):
Pavan Kantharaju and Sonja Schmer-galunder. 2022. Extracting Associations of Intersectional Identities with Discourse about Institution from Nigeria. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 164–169, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Extracting Associations of Intersectional Identities with Discourse about Institution from Nigeria (Kantharaju & Schmer-galunder, NLP+CSS 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.nlpcss-1.18.pdf