key: cord-0227663-0nlhwdjs authors: Debnath, Ramit; Bardhan, Ronita title: India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling date: 2020-05-14 journal: nan DOI: nan sha: 7916c1645a9d7ae537e6f6a631ee736216bea57a doc_id: 227663 cord_uid: 0nlhwdjs 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. India locked down 1.3 billion people on March 25, 2020 , in the wake of novel coronavirus COVID-19 pandemic. The Prime Minister of the country, Mr Narendra Modi, in his address to the nation, appealed to the nation that '… 21 days is critical to breaking the infection cycle… or else the country and your family could be set back 21 years…' (1). In a sense, the government used the nudge of 'nationalism' as an effective measure to control the disease spread. This nudge had critical public policy implications because it successfully convinced 1.3 billion population to abide by lockdown rules at high economic and social costs. The estimated economic cost of the Phase 1 lockdown of 21 days ( March 25 to April 14, 2020) was estimated to be almost USD 98 billion (2) . While nudging is a designbased approach that has been used in several domains for priming human behaviour, it is often used as the 'best-guesses', to tailor government policies (3) . It is challenging to ascertain the reliability and replicability of similar nudge in producing desired behaviour change. Nevertheless, it is imperative to untangle the nudges produced by the government policies for coping future national challenges like COVID- 19 . In this case, understanding how the central Government of India (GoI) informed policies to handle the ongoing national crisis is crucial for lockdown mitigation planning. It has implications in planning resilience and future-proofing extensive national emergencies. Big data and machine learning have proven to be a reliable technique in mining and distilling patterns in data and transform into predictive analytics. This technique has the promise to filter intricate information into meaningful behaviour metrics and hence could be applied to harvest intricate nudges from policy datasets that will warrant replicability of desired behaviours. This study intends to explore the response of the GoI since the outbreak of COVID-19 in the country by collecting open public data published by the Government's nodal agency -Press Information Bureau of India (PIB) (4). The social data-science methodology of topic modelling based on text processing was employed (5) to examine the key areas of interventions that were deliberated by various ministries of GoI during the emergency phase. The topics of interventions which eventually nudged the citizen behaviours were extracted from a textual policy database since the emergence of the first case in the country, on January 30, 2020. The probabilistic distribution curves generated through Latent Dirichlet Allocation (LDA) algorithm was used for texts mining (6) on a text-database, prepared from the press releases, published between January 15, 2020, and April 14, 2020. These topics were then processed to elaborate government nudging for influencing citizen's behaviour in the wake of coronavirus pandemic. Topic modelling is a widely used computational social science method that has its basis in text mining and natural language processing. It is an unsupervised machine learning technique that automatically analyses text data to determine cluster words for a set of documents (7) . Topic modelling (TM) has garnered significant importance in political science and rhetoric analysis (8) . Researchers have used TM to investigate reactions of different political communities on the same news for understanding political polarisation in the United States (9) . Similarly, in Korea, Kim & Jeong (10) have used TM on twitter dataset to analyse the temporal variation of the socio-political landscape of the 2012 Korean Presidential Election. In Germany, researchers have used a TM-approach to explore the multi-dimensionality of political texts and the discourses of public policies since National Elections of 1990 (11) . This study aided in understanding the polarising shifts in policy interventions that modulated the political narratives in Germany. More recent applications of TM includes crisis identification in urban areas for evidence-based policymaking (12) , deep narrative analysis for deriving intervention points for distributive energy justice in poverty (13) and informed public policy design in public administration (14) . None of the above application of TM has explored the policy reactions of a government towards handling a national emergency. Although TM entails sophisticated extraction of topics by algorithmically evaluating their 'relevance', integrating this as a guide for future nudging can produce the desired 'priming' and 'selective optimisation'. In general, the intention is to use nudge tactics as a solution to the last-mile problem, i.e. the gap between people's long-term intentions and their everyday actions, for meeting future challenges. The integration of TM for nudge identification from government policy documents defines the novelty of this study. This study will aid policymakers and government agencies in India to plan lockdown easement from a multi-dimensional public policy perspective. The policy inferences presented in this study is also critical for other countries that are affected by the COVID-19 crisis and under extended lockdown. Data for this study were collected from the media releases of policies and plans of different ministries in the Press Information Bureau (PIB) platform (4). English news and information with the keyword 'coronavirus', 'COVID', 'COVID-19' and 'nCoV' was collected and aggregated in a text format from January 15, 2020, and April 14, 2020. Manual filtering of the press and media releases based on the above keywords resulted in 396 documents from around 42 ministries of the Government of India. The entire text corpus from these documents consisted of 260,852 words. We classified these documents into 14 public policy categories, as illustrated in Table 1 . Besides, we have also included COVID-19 briefings from the Prime Minister's Office in the policy categories (see Table 1 ). Topic modelling refers to the task of identifying topics that best describes a set of documents. It is an unsupervised machine learning technique that automatically analyses text data to determine cluster words from a set of documents. It is based on the basic idea that each document can be expressed as a distribution of topics, and each topic can be described by a distribution of words (6) . The basic terminology used in LDA is based on the language of 'text collection', referring to entities such as "words", "documents" and "corpora". These terms are defined as (after (6)), • A word is the basic unit of discrete data, defined to be an item from a vocabulary indexed by {1, … , }. We represent words using unit-basis vectors that have a single component equal to one and all other components equal to zero. Thus, using superscripts to denote components, the vth word in the vocabulary is represented by a V-vector w such that = 1 and = 0 for ≠ . • A document is a sequence of N words denoted by = ( 1 , 2 , … , ), where is the nth word in the sequence. • A corpus is a collection of M documents denoted by = { , , … , }. Latent Dirichlet allocation (LDA) is a Bayesian mixture model for discrete data in which topics are uncorrelated. The objective of topic modelling is to extract latent semantic topics from large volumes of textual documents (i.e., corpora). LDA is a widely used topic modelling (TM) technique, with recent applications spanning across political science and rhetoric analysis (8) (9) (10) 15, 16) , disaster management (12, 17, 18) and public policy (13, 14, 19) . Fig 1 illustrates the probabilistic graphical model of LDA, and the probability calculation formula is illustrated in eq. 1. (1) where, the boxes in Fig 1 are " plates" representing replicates. The outer plate represents documents (M), while the inner plate represents the repeated choice of topics (z) and words (w) within a document (N). 'ϴ' is the topic distribution for document, i.e. 'α', 'β' are two hyperparameters of the Dirichlet distribution (see eq. 1). The third hyperparameter is the 'number of topics' that the algorithm will detect since LDA cannot decide on the number of topics by itself. We use our judgement and the ldatuning package (20) in R to determine the number of topics in each of the topic models (discussed later in detail). The analysis consisted of three main steps. The first step was the pre-processing of the documents by removing all the stop words (e.g., articles, such as "a," "an," and "the," and prepositions, such as "of," "by," and "from"), numbers, and punctuation characters and converted the text to lowercase in the corpora. And some general words appear in most of the government media releases like "name of ministers", "secretary", "union government" and courtesy words like "Shri", "honourable", "respected", "sir" and "thank you". We constructed a list of additional stop words that were colloquial terms in Indian-English and removed them from the text-corpus. This step is usually called lemmatisation (21) . Lemmatisation also involved removal of inflectional ending of words, and converting the grammatical form of a word into the base or dictionary form (known as Lemma) (21) . The second step was to fit the model using the lemmatised corpora. Using the tidytext package in the R programming language, we converted the article into a document-term-matrix (DTM) as per the specification of tidydata rules (see chapter 1, (22) ). Each sentence was treated as a document in the DTM, that resulted in (M) unique documents that had w (words) and z (topics) as per LDA probability model specification (see eq. 1 and Fig 1) . We adopted an iterative approach where we first specified Griffiths and Steyvers (5) and Deveaud et al., (25) . These metrics were part of the ldatuning package in R (20) ; similar approach was also adopted by (14, 19) . We used the R package topicmodels to fit the LDA model (26) . The third step included visualisation and manual validation of the topics. For visualisation, we have used the ggplot2 package in R (27) . We have also estimated and visualised co-occurrence of highfrequency keywords in the corpora using the methodology of Jan van Eck and Waltman (28). The extracted topic was further analysed and interpreted concerning reactive policy steps using the epistemology of nudge theory in behavioural public policy (29). Nudge theory is mainly concerned with the design of choices, which influences the decisions we make. It seeks to improve understanding and management of the 'heuristic' influences on human behaviour which is central to 'changing' people (30) . Epistemologically, Thaler and Sunstein (30) used nudge policies and interventions as an application of a conceptual framework called libertarian paternalism. The authors contend that retaining the freedom to choose is the best safeguard against a misguided policy intervention. The 'nudging' approach is paternalistic in the sense of motivating behaviour change that aligns with the target population's deliberative preferences (29) . Thus, libertarian paternalism relies on the assumption that each human being makes many decisions automatically and almost unthinkingly each day by following some innate rules of thumb (29) . It had been reported in literature that from a policy-instrumentation perspective, nudges constitute a less coercive form of government intervention compared to more traditional policy tools such as regulations and taxations (31) . While policy interventions can provide the right directions, it cannot suggest the promptness of the behaviour change. The behavioural nudge tactics, here, enable solving this last mile problem of policy intervention implementation success through the use of "soft" A comprehensive literature review on the application of nudge theory in public policy and public management by Van Deun et al., (31) found that about 40% of the articles linked to health policies and almost 20% related to environmental policies. Other nudge sector included land and rural policies, financial policies, transport policies, law, social security, education and digitalization policies (31) . More importantly, this theory has been in practice in the British Government (now independent) through the Behavioural Insight Unit (also known as the 'Nudge' Unit) (32). The nudge theoretic approach has been used by the British Government to tackle the early stages of coronavirus pandemic in the UK (33) . The behavioural nudges that were deliberated to the public included 'wash your hands, do not touch your face, do not shake hands with others, stay at home if you feel ill, and self-isolate if you have a continuous cough' (33) . Through this study, we wanted to understand how the Government of India used nudges as a public policy measure to fight the coronavirus outbreak. A keyword co-occurrence network was constructed with the 260,852 words corpora that shows a connected network of high-frequency words (see Fig 1) . Words or terms that were mentioned at least 50 times in the text corpus were considered as high-frequency words. The co-occurrence representation has two components. High-frequency keyword co-occurrence representation on media briefings from Press Information Bureau (PBI) of the Government of India (GoI) in the wake of Covid-19 pandemic (mid-January 2020 to mid-April 2020). Words that were repeated at least 50 times in the text corpus were considered in this analysis (n = 260,852). We have individually analysed and modelled the content of press releases from different ministries by classifying them in 14 policy categories (see Table 1 ). In doing so, we estimated the approximate number of topic models for each of the policy categories using the benchmarking metrics of Arun2010 (23), CaoJuan2009 (24), Griffiths2004 (5) and Deveaud2014 (25) , as illustrated in Table 3 . The approximation of the number of topics was also made through judgement, where, we found that increasing the number of topics was affecting the interpretability of the topic models. High-frequency words within the ministries are illustrated in Fig 3. The policies on agriculture and farmer's welfare focussed on ensuring food security and undisrupted supply chain during the nationwide lockdown phase (see Fig 3) . February to April is the harvesting time for winter crops in India that is crucial for food security in the country. In the wake of coronavirus and strict lockdown measures, the GoI allowed farmers to harvest. Besides, policy emphasis was laid on providing fiscal packages to the distressed farmers who were affected by national lockdown and supply chain disruption. Topic extraction through LDA (see Table 4 ) showed that the policy nudges were focussed on the continuity of harvest (topic 1, 'harvest', β= 0.030) and rerouting of the critical food supply chain (topic 2, 'lakh', β = 0.100) during the extended lockdown period for ensuring food security (topic 1, 'food security', β = 0.150). AYUSH is an acronym for Ayurvedic, Yoga and Naturopathy, Unani, Siddha and Homeopathy. In the early stages of coronavirus pandemic in the country, this ministry released a series of press releases nudging people to follow the traditional medicinal practice of Ayurveda and maintaining good health and well-being through yoga (see Fig 3) . The policy nudges, as revealed by the topic (see Table 4 ), showed a greater emphasis on increasing immunity through ayurvedic and herbal products. The topics also revealed higher stress on using Homeopathy (β = 0.018) and Ayurveda (β = 0.032) as preventive measure along with disciplined personal hygiene. It was observed that from the media releases that between January and the first week of March, AYUSH policies were aggressively nudging the use of traditional route to treat COVID-19. However, there was a shift in narrative during the mid-March as India experienced high infection rates. It focussed on promoting a healthy lifestyle through policy nudges using hashtags like #YOGAathome (see box 1). Box 1. AYUSH nudges on preventive health measures and boosting immunity (source: (34)) The high-frequency word cloud for 'chemical' policy sector (see Table 1 and Fig 3) revealed higher policy stress on the availability of therapeutic drug and medical devices like ventilator and lifesaving equipment. Greater policy nudges were on empowering and motivating the manufacturing sector to contribute to medical device availability in the wake of coronavirus pandemic (see Fig 3) . Three topic models were extracted that further expands on the policy nudges in this sector (see Table 4 ). Topic 1 indicates a greater emphasis on the bulk supply of medicine (β = 0.065) and contribution to the PM-CARES fund to ensure medicine availability in the country. Topic 2 further illustrates the aggressive nudging in manufacturing medical devices (β = 0.048). In addition, LDA extracts in Topic 3 revealed the higher impetus on supporting the frontline workers, see 'mask (β = 0.048)', 'PPE (personal protective equipment) (β = 0.045)', 'sanitiser (β = 0.036)' and 'drug surplus (β = 0.030) (see Table 4 ). The nudges from electronics and IT related policies were aggressive on tackling fake news in social media and keeping people indoors during the lockdown (see Fig 3) . The repeated telecast of popular '80s and '90s TV shows were one of the distinct public policy nudges. It used nostalgia as a nudge to make the people conform to stay at home norm and practice social distancing measures (35) . These TV-shows ranged from family entertainer to religious and were broadcasted in the national channel called Doordarshan. Four topics were extracted (see Table 4 ), of which, topic 1 shows 'fake news' around COVID-19 as a high probability term (β = 0.070). It is being treated as a concern of national security. Topic 2 showed a similar discourse on guidelines concerning social media usage (β = 0.025) and fake news control (β = 0.050) through the Ministry of Electronics & IT. As aforementioned, India's public broadcaster DD aired '80s epic Hindu tale 'Ramayana' and 'Mahabharata' as a selfquarantine measure (35) . It is an application of nudging-based public policy measure referred to as the herd effect (29) , illustrated in Topic 3 (see Table 4 ). Besides, various fiscal measures were taken to support the continuity of information flow through print and electronic media during the quarantine Table 4 ). Four topics were extracted, where online learning (Topic 1 and Topic 3) and work from home (Topic 4, β = 0.040) were the highest frequency words (see Table 4 ). The topic 1 illustrated the policy focus on infrastructure setup and Policy nudges in the power and energy sector were mostly dedicated to collecting funds for PM-CARES (see Fig 3) . The extracted topics are illustrated in Table 4 Social justice in the wake of coronavirus pandemic is a critical policy focus point. Nudges included social security of migrant workers, labourers and women-led self-help group (see Fig 3) . Particular guidelines were released for the person with a disability (see Table 4 Ministry of Home Affairs and Ministry of Defence are the institutions that deal with national security and peacekeeping. In this study, we combined the press releases of both the ministries as 'Home Affairs' (see Table 1 ) as they have been working in tandem governing the national lockdown rules in the wake of coronavirus pandemic. Fig 3 shows the high-frequency words from the home affairs. It exhibited ensuring the supply of essential commodities, ensuring lockdown governance, surveillance measures and quarantine facilities as highlighted words. Ten topic models were extracted using LDA, as illustrated in Table 5 . Topic 1 (see Table 5 planes were used to transport medicines, PPE, masks and life-saving devices across the nations (see Table 5 ) The MHA was also extensively involved with the manufacturing sector to design and develop low-cost ventilators, PPE, sanitisers and masks (see Topic 5 and Topic 6, Table 5 ). Extensive nudging was done to ensure that the government was actively involved in delivering essential items by engaging with the supply chain of Indian Railways (see Topic 6) . Moreover, amidst the national lockdown, spikes in coronavirus cases were observed in New Delhi due to religious gathering (Tablighi Jamaat congregation), the MHA had to tighten up surveillance and increase the nationwide contact tracing (see Topic 7) . This event was speculated as to India's worst coronavirus vector (36) . Besides, MHA ensured surveillance at the airports and international borders and were the first responders during the early stage of the pandemic in the country (see Topic 8) . It used nudging at the airport to ensure travellers maintain a 14-day home quarantine by stamping people with 'Home Quarantine 'on forearms (see Box 2). Box 2. 'Home quarantined' stamp for travellers as nudging for self-isolation (source: (37)) Furthermore, Topic 9 and Topic 10 (see Table 5 The surveillance in urban areas was done using smart technologies (see Fig 3) that included drones, spatial analysis, low-power Bluetooth mobile phone applications and humanoid robots (39) . The Smart City program of India (40) Table 6 for the topics extracted by LDA concerning urban policies. The significant policy nudges were on requesting the public to comply with the strict quarantine rules using drones and smart surveillance technologies (see Table 6 and Box 3a). Nudging was also on the use of COVID-19 contact tracing apps, and GIS-based methods for monitoring quarantined public at a municipality level. Special attention was given to the routine solid waste collection, transportation and disposal activities along with cleaning and scrapping were carried out efficiently to keep the cities clean. In few highly dense urban centres, disinfection tunnels were installed (see Box 3b) with facilities of thermal screening by taking temperature. Pedestal operated hand-wash and soap dispenser, mist spray of sodium hypochlorite solution and hand dryer facility. The topic extracted in table 6 compiles all these measures to control the spread of COVID-19. The transportation sector played a critical role in maintaining the supply chain of essential items. Fig 3 shows the high-frequency words in the transportation sector that includes freight transport, railways, shipping and road and highways. The topics extracted by LDA is illustrated in table 7 with the policy nudges in the transportation sector in the wake of coronavirus pandemic in India. The National Institute of Virology (NIV) was at the forefront of testing, which provided the technical guidance for testing labs across the country (see Table 8 ). Academic and research institutions were encouraged to submit competitive interdisciplinary research proposals to focus on the development of affordable diagnostics, vaccines, antivirals, disease models, and other R&D to study COVID-19 (see Table 8 ). Scientific innovation during this period includes robots for encouraging social distancing in public spaces and healthcare centres (see Box 5) . A contact tracing app (AarogyaSetu) using GPS and Bluetooth to inform people when they are at risk of exposure to COVID-19. Low-cost, easy-to-use, and portable ventilators that can be deployed even in rural areas of India. To nudge people into using the application was provided by frequent reminders through SMS. Innovations were also done in ensuring public-space hygiene through the development of water-based sanitiser disinfectant and technology to dispenses ionised water droplets to oxidise the viral protein (45). The DST set up a task force to map technologies developed by start-ups related to COVID-19. It is funding start-ups to develop relevant innovations such as rapid testing for the virus (see Table 8 ) Table 9 shows the topic extracted by LDA in the health sector between January to April. The results show that in January, the policy nudges were in evaluating the risk of incoming travellers coming from China and extending surveillance at international airports. High-frequency words associated with such nudges can be seen in Fig 4. The change in policy narratives of the health ministry can be seen with the spread of infection in the country (see February, Table 9 ). The nudges were on enhancing thermal screening at airports of international arrivals and imposing travel restriction (see Fig 4) . Furthermore, topics extracted for February also indicates the beginning phase off restrictions such as advisory on social distancing and frequent hand washing as a possible preventive measure of towards COVID-19 infection (see Table 9 ). In additions, Manufacturing units were asked to produce PPE, hand sanitiser and masks to meet the national demand (see Table 9 , March). The Indian Council of Medical Research (ICMR) was the nodal agency for coordinating with press and MoHFW concerning the development regarding COVID-19 pandemic. It started daily briefing on government policies and preparedness on fighting coronavirus (see March, Table 9 and Fig 4) . The policy nudges for April was centred towards strengthening the COVID-19 specific healthcare requirements. Increasing the number of testing done per 1000 people was one of the significant agenda along with the social distancing measures. This phase was also marked by innovation in indigenous science and technology for empowering frontline working to fight COVID-19 (see Table 8 and Table 9 ). During this period, policy nudges were also towards ensuring food security and availability of essential items and medicines across the nation (see Fig 4) . Masks were made compulsory at public spaces across the nation (see Table 9 , April and Fig 4) . Table 10 ). In this process, the PMO spearheaded higher-income groups to look after the economic needs of those from lower-income groups, from whom they take various services, urging them not to cut their salary on the days they are unable to render the services due to inability to come to the workplace. PM stressed on the importance of humanity during such times (47) . The topics extracted by LDA on PMO is illustrated in Table 9 . We studied the reactive public policies in India in the wake of coronavirus pandemic through topic modelling using LDA. The reactiveness of public policies across the policy sectors (see Table 1) was done through the lens of nudge theory. The extracted topic models (TM) by an unsupervised machine learning method called Latent Dirichlet Allocation (LDA) aided in gaining deeper insights into the nudges made by various policymaking bodies (illustrated through Table 4 to Table 9 ). Besides, we have analysed the high-frequency words (see Fig 3) to have a better bird's eye view of the public policy focus points in the wake of COVID-19 in India. High probability (β) words across 14 policy sectors (see Table 1 ) illustrated the heuristics of policymaking in containing the virus spread. The extraction of heuristics revealed that commonalities in policy nudges were on enforcing lockdown rules, improving surveillance and encouraging the public to wear masks and wash hands frequently. Sector-specific heuristic focussed on maintaining equilibrium within the sector. For example, in the agriculture sector, a critical nudge on allowing the harvest of winter crops for food security amidst lockdown (see Table 4 , Agriculture and Food, Topic 1). Heuristics were also extracted in the traditional medicine and well-being sector, that nudged people with #YogaAtHome and Ayurveda for immunity boosting (see Box 1). These nudges were also towards promoting a healthier lifestyle through traditional medicines and practices, that will be important even in post-COVID scenarios. The public policy nudges in the chemical sector were on ensuring drug surplus, whereas more nudges were given to the industry to fulfil the shortage of medical devices and ventilators. Preservation of the medical supply chain was a critical heuristic. However, the coronavirus pandemic further created a demand for an efficient supply chain of personal protective equipment (PPE), sanitiser and masks (see Table 4 , Chemicals). In doing so, new heuristics were added by nudging rural micro, small and medium enterprises (MSMEs) to join the fight against coronavirus by mass-producing PPE and masks. It had critical social justice implications, especially in rural areas where women-led self-help groups are the primary workforce in such MSMEs (see Table 4 , social justice). Nudges on the use of AYUSH-based herbal and traditional products also catered to this rural SME ecosystem which is critical for the survival of the economy in the pandemic. Besides, the populist Prime Minister (PM) frequently nudged the nation on staying at home, adhering to lockdown rules, improving immunity through yoga and Ayurveda and contributing to the PM-CARES fund (see Table 9 ). A herd effect was created through such nudges where public participation and micro-donations led the fight against Covid-19. Similar nudges for micro-donations through herd effect was also seen in other critical sectors like the manufacturing, commerce, power, construction and pharma. Topic extractions also showed herd effect-based policies in the education sector, especially with a higher emphasis on online learning and #StayHomeWithBooks initiatives by the Ministry of Human Resource Development (see Table 4 , MHRD). Public broadcasters began to air 80s epic Hinduepic for herd effect on staying at home with family. Nudges through 'nostalgia' was a significant reactive policy step by the Ministry of Information and Broadcasting (see Table 4 . Electronics & IT) to motivate self-isolation. Reactive policies were also seen in the urban sector that nudged municipal authorities to leverage smart technologies like drones for disinfection and surveillance, GIS-platforms and contact tracing apps (see Table 5 and Box 3). A herd-effect was also created in the science and technology (S&T) community of India through funding R&D of diagnostic kits, disinfectant coating, crowdsourcing ideas and innovation challenges (see Table 7 ). Health sector policies focused on aggressive nudging the public to wear homemade masks, maintain social distancing and adhere to hand hygiene rules (see Table 8 ). The herd-effect was on sensitising people on the severity of Covid-19 transmission for 1.3 billion people. The Indian Railways acted as a lifeline in ensuring the resilience of the supply chain of essential goods and rapid infrastructure development by converting old trains into isolation wards (see Box 4 and Table 6 ). Similarly, the Ministry of Defence and Ministry of Civil Aviation showed reactive policies through joint operations on-air delivery of essential medicine and devices through 'Lifeline UDAAN' mission (see Table 6 ). It created a herd effect on food and medicine security amongst the public that in turn prevented from hoarding on to essential goods. A critical heuristic in ensuring public follows the national lockdown norms that enabled the efforts of Ministry of Home Affairs (see Table 4 ). Our LDA application identifies the herd-effects and policy nudges that can aid in lockdown easement planning, as aforementioned. Similar nudge-based policy approach is especially crucial in a democracy in India with a vast demographic and geo-spatial divide. This study showed an application of topic modelling for public policy. Our application of LDA on government press releases extracted topics across core policy sectors in India that acted as critical nudges in the wake of coronavirus. Use of LDA in such media-data based policy analysis showed its strength in extracting topics that have high concordance with the broader narrative of the government. Our analysis showed that these narratives and nudges created herd effects that motivated the nation of 1.3 billion people to stay home during the national lockdown, even with high economic and social costs. The integration of computational social science tools like the LDA for identifying nudges for channelising public behaviour through reactiveness of public policy in the wake of coronavirus outbreak expands the scope of machine learning and AI for public policy applications. From a behavioural public policy perspective, the stochastic interpretation of the topic models through LDA derived critical policy heuristics that must be leveraged during the lockdown easement planning. We believe we are the first in applying LDA to account the reactiveness of Covid-19 induced public policy at multi-sectoral scale. The key conclusions that can be drawn from this study are: • The use of rigorous media campaigns primarily generated the herd behaviour for successful containment of COVID-19, frequent reminders through SMS, publicising data-driven risk maps generated from innovation grants, public reassurances by the medical community and invoking the feeling of nationalism and solidarity. • Most of the interventions were targeted to generate endogenous nudges by using external triggers which potentially produces lasting desired behaviour in repeat settings (i.e. repeated broadcasting of information through multi-media channel) and hence can be applied in toto for future challenges. • Prime Minister's frequent public appearances and assurances nudged in creating the herd effect across pharma, economic, health and public safety sectors that enabled strict national lockdown. It created a herd effect of public participation and micro-donations to the PM-CARES fund to fight the pandemic. • Successful herd effect nudging was observed around the public health sector (e.g., compulsory wearing of masks in public spaces; Yoga and Ayurveda for boosting immunity), transport sector (e.g., old railway coaches converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks for frontline words), science and technology sector (e.g., the rapid development of indigenous diagnostic kits, use of robots and nano-technology to fight infection), home affairs (e.g., people adhering to strict lockdown rules even at high economic distress), urban (e.g., drones, GIS-mapping, crowdsourcing) and education (e.g., work from home and online learning). • Similar nudging-based approach to the public policy during lockdown easement planning can aid in the smooth yet staggered transition to normalcy. It can even provide a way forward for reviving the economy and climate change mitigation goals. • LDA can extract topics that have high concordance to nudges making it a suitable tool to study reactiveness of behavioural public policies. While this study showed the application of topic models in reactive public policy analysis, the inherent limitations of unsupervised topic modelling remain in the analysis. It interprets the topic models sensitive to the viewpoint of the analysts. Besides, the official press releases used in this study as the primary dataset may contain confirmatory biases, removal of such biases was beyond the scope of this study. Moreover, the press releases in the Press Information Bureau platform lacked granularity as they are intended for informing the public and media. In our future work, we are exploring the detailed policy documents to improve the clarity of topic models by sector. Text of PM's address to the nation on Vital aspects relating to the menace of World's biggest lockdown may have cost Rs 7-8 lakh crore to Indian economy. online Big Data as a mode of regulation by design Finding scientific topics Latent Dirichlet Allocation Navigating the Local Modes of Big Data: The Case of Topic Models Text as data: The promise and pitfalls of automatic content analysis methods for political texts Modeling polarizing topics: When do different political communities respond differently to the same news Analyzing the political landscape of 2012 korean presidential election in twitter Multidimensional topic analysis in political texts Tracking urban geo-topics based on dynamic topic model Built Environment and Poverty: A Deep-narrative Analysis of Energy Cultures in Brazil, India and Nigeria for Policy Modelling Topic Modeling the Research-Practice Gap in Public Administration Predicting response to political blog posts with topic models. NAACL HLT 2009 -Hum Lang Technol Muslims in social media discourse: Combining topic modeling and critical discourse analysis. Discourse, Context Media [Internet DUET: Data-Driven Approach Based on Latent Dirichlet Allocation Topic Modeling A multiscale latent dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images Leveraging Latent Dirichlet Allocation in processing free-text personal goals among patients undergoing bladder cancer surgery ldatuning: Tuning of the Latent Dirichlet Allocation Model Parameters: R package version 0 Introduction to Information Retrieval Text Mining with R: A Tidy Approach On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations A density-based method for adaptive LDA model selection Accurate and Effective Latent Concept Modeling for Ad Hoc Information Retrieval topicmodels: An R Package for Fitting Topic Models Citation-based clustering of publications using CitNetExplorer and VOSviewer The Origins of Behavioural Public Policy Nudge: Improving Decisions About Health, Wealth, And Happiness Nudging in Public Policy and Public Management: A scoping review of the literature Why is the government relying on nudge theory to fight coronavirus? AYUSH reiterates immunity boosting measures for self-care during COVID 19 crises Coronavirus Lockdown Creates Captive Audience for '80s Show How Tablighi Jamaat event became India's worst coronavirus vector Proud to protect…" quarantine stamps for passengers at Mumbai, Delhi, Bengaluru airports States are making best use of technology to combat covid-19 Coronavirus pandemic: India's Covid combat gets a tech tonic Ministry of Housing and Urban Affairs How India's Smart Cities are fighting against Centre strongly advises against spraying of disinfectants on people India has closed its railways for the first time in 167 years. Now trains are being turned into hospitals PSA. Masks for Curbing the Spread of SARS-CoV-2 Coronavirus: A Manual on Homemade masks India: how coronavirus sparked a wave of innovation Robots may become heroes in war on coronavirus PM at the helm of India's Fight against COVID-19 Scholarship under the Grant no.[0PP1144] at the University of Cambridge, awarded to RD. Any opinion, findings and/or conclusion are that of the authors and do not necessarily reflect the views of the funding bodies or affiliated organisations. All authors have contributed equally.