India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

Ramit Debnath · Ronita Bardhan

105 citations0/0 files storedPLoS ONE

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

COVID-19 Prevention and Impact Public Administration and Political Analysis Communication and COVID-19 Impact

Abstract

India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 105 citations exist upstream.

References

2008Nudge: improving decisions about health, wealth, and happiness 2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts 2017Citation-based clustering of publications using CitNetExplorer and VOSviewer 2011topicmodels : An R Package for Fitting Topic Models 2016‘Hypernudge’: Big Data as a mode of regulation by design 2017Text Mining with R: A Tidy Approach 2016Muslims in social media discourse: Combining topic modeling and critical discourse analysis 2016Navigating the Local Modes of Big Data: The Case of Topic Models

Cited by

2022Evaluating health policies with subnational disparities: a text-mining analysis of the Urban Employee Basic Medical Insurance Scheme in China 2025Ethical Algorithms for Machine Learning Based Ai Powered Robotics: Ethical Perspective for Covid-19 Like Health Emergencies 2023On subsistence‐type rural independent retailers and crowdfunded microfinance—Prosocial lending, nudges, and unintended consequences 2021The Rationale of Gentle Nudges and its Reflections in the Work Behaviour of Corporate Employees during the Outbreak of Pandemic

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.