Identifying Human Needs through Social Media: A study on Indian cities during COVID-19
Sunny Rai, Rohan Joseph, Prakruti Singh Thakur, Mohammed Abdul Khaliq
Abstract
In this paper, we present a minimally-supervised approach to identify human needs expressed in tweets. Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of unmet needs. Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs. Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India. Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis.- Anthology ID:
- 2022.socialnlp-1.6
- Volume:
- Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Lun-Wei Ku, Cheng-Te Li, Yu-Che Tsai, Wei-Yao Wang
- Venue:
- SocialNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–74
- Language:
- URL:
- https://aclanthology.org/2022.socialnlp-1.6
- DOI:
- 10.18653/v1/2022.socialnlp-1.6
- Cite (ACL):
- Sunny Rai, Rohan Joseph, Prakruti Singh Thakur, and Mohammed Abdul Khaliq. 2022. Identifying Human Needs through Social Media: A study on Indian cities during COVID-19. In Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media, pages 65–74, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Human Needs through Social Media: A study on Indian cities during COVID-19 (Rai et al., SocialNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.socialnlp-1.6.pdf
- Code
- axleblaze3/covid_19_tweets_with_tagged_needs