“#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter

Ishani Mondal, Sukhnidh Kaur, Kalika Bali, Aditya Vashistha, Manohar Swaminathan


Abstract
Twitter serves as a powerful tool for self-expression among the disabled people. To understand how disabled people in India use Twitter, we introduce a manually annotated corpus #DisabledOnIndianTwitter comprising of 2,384 tweets posted by 27 female and 15 male users. These users practice diverse professions and engage in varied online discourses on disability in India. To examine patterns in their Twitter use, we propose a novel hierarchical annotation taxonomy to classify the tweets into various themes including discrimination, advocacy, and self-identification. Using these annotations, we benchmark the corpus leveraging state-of-the-art classifiers. Finally through a mixed-methods analysis on our annotated corpus, we reveal stark differences in self-expression between male and female disabled users on Indian Twitter.
Anthology ID:
2022.findings-aacl.35
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
375–386
Language:
URL:
https://aclanthology.org/2022.findings-aacl.35
DOI:
10.18653/v1/2022.findings-aacl.35
Bibkey:
Cite (ACL):
Ishani Mondal, Sukhnidh Kaur, Kalika Bali, Aditya Vashistha, and Manohar Swaminathan. 2022. “#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 375–386, Online only. Association for Computational Linguistics.
Cite (Informal):
“#DisabledOnIndianTwitter” : A Dataset towards Understanding the Expression of People with Disabilities on Indian Twitter (Mondal et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-aacl.35.pdf