Identifying Human Needs through Social Media: A study on Indian cities during COVID-19
10th International Workshop on Natural Language Processing for Social Media, SocialNLP 2022
; : 54-63, 2022.
Article
in English
| Scopus | ID: covidwho-2073637
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. © 2022 Association for Computational Linguistics.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
10th International Workshop on Natural Language Processing for Social Media, SocialNLP 2022
Year:
2022
Document Type:
Article
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