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Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining

The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villag...

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Autores principales: Misra, Pooja, Gupta, Jaya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294302/
https://www.ncbi.nlm.nih.gov/pubmed/34305343
http://dx.doi.org/10.1007/s41027-021-00324-y
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author Misra, Pooja
Gupta, Jaya
author_facet Misra, Pooja
Gupta, Jaya
author_sort Misra, Pooja
collection PubMed
description The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villages started. Mental, financial and emotional agony inflicted due to job-loss, lack of job and livelihood opportunities led to this. A massive macroeconomic crisis for the country with serious ramifications has consequently exploded. The present study explores and captures the diffusion and discovery of information about the various facets of reverse migration in India using Twitter mining. Tweets provide extensive opportunities to extract social perceptions and insights relevant to migration of workers. The massive Twitter data were analysed by applying text mining technique and sentiment analysis. The results of the analysis highlight five major themes. The sentiment analysis confirms the confidence and trust in the minds of masses about tiding through this crisis with government support. The study brings out the major macroeconomic ramifications of this reverse migration. The study’s findings indicate that a concentrated joint intervention by the State and Central Governments is critical for successfully tiding through this crisis and restoring normalcy. The subsequent policy measures announced by the government are being critically gauged. In addition, the authors have proposed measures to ameliorate this damage on the formal and informal sectors.
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spelling pubmed-82943022021-07-21 Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining Misra, Pooja Gupta, Jaya Indian J Labour Econ Research Note The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villages started. Mental, financial and emotional agony inflicted due to job-loss, lack of job and livelihood opportunities led to this. A massive macroeconomic crisis for the country with serious ramifications has consequently exploded. The present study explores and captures the diffusion and discovery of information about the various facets of reverse migration in India using Twitter mining. Tweets provide extensive opportunities to extract social perceptions and insights relevant to migration of workers. The massive Twitter data were analysed by applying text mining technique and sentiment analysis. The results of the analysis highlight five major themes. The sentiment analysis confirms the confidence and trust in the minds of masses about tiding through this crisis with government support. The study brings out the major macroeconomic ramifications of this reverse migration. The study’s findings indicate that a concentrated joint intervention by the State and Central Governments is critical for successfully tiding through this crisis and restoring normalcy. The subsequent policy measures announced by the government are being critically gauged. In addition, the authors have proposed measures to ameliorate this damage on the formal and informal sectors. Springer India 2021-07-20 2021 /pmc/articles/PMC8294302/ /pubmed/34305343 http://dx.doi.org/10.1007/s41027-021-00324-y Text en © Indian Society of Labour Economics 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Note
Misra, Pooja
Gupta, Jaya
Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title_full Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title_fullStr Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title_full_unstemmed Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title_short Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining
title_sort impact of covid 19 on indian migrant workers: decoding twitter data by text mining
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294302/
https://www.ncbi.nlm.nih.gov/pubmed/34305343
http://dx.doi.org/10.1007/s41027-021-00324-y
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