Sohini Sengupta

Bio: Sohini Sengupta is an academic researcher from Tata Institute of Social Sciences. The author has contributed to research in topics: Social policy & Storm. The author has an hindex of 2, co-authored 4 publications receiving 53 citations. Previous affiliations of Sohini Sengupta include Prin. L. N. Welingkar Institute of Management Development and Research.

Social Policy, COVID-19 and Impoverished Migrants: Challenges and Prospects in Locked Down India:

Abstract: As countries shore up existing safeguards to address the social and economic impacts of the COVID-19 pandemic, India faces a humanitarian disaster of unprecedented proportions Ninety per cent of t

Covid-19 Pandemic Data Analysis and Forecasting using Machine Learning Algorithms

Abstract: India reported its first Covid-19 case on 30th Jan 2020 and the number of cases reported heavily escalated from March, 2020 This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India Data is gathered from multiple data sources-several authentic government websites The need of the hour is to accurately forecast when the numbers will reach at its peak and then diminish It will be of huge help to public welfare professionals to plan the preventive measures to be taken keeping the economic balance of the country as well Variables such as gender, geographical location, age etc have been represented using Python and Data Visualization techniques Time Series Forecasting techniques including Machine Learning models like Linear Regression, Support Vector Regression, Polynomial Regression and Deep Learning Forecasting Model like LSTM(Long short-term memory) are deployed to study the probable hike in cases and in the near future A comparative analysis is also done to understand which model fits the best for our data Data is considered till 30th July, 2020 The results show that a statistical model named sigmoid model is outperforming other models Also the Sigmoid model is giving an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten Strength of Sigmoid model lies in providing a count of date that no other model offers and thus it is the best model to predict Covid cases counts –this is unique feature of analysis in this paper Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals

An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

Abstract: Twitter is one of the world’s biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users. Executive Summery Novel Corona virus’s spread and its impact on various aspects of national and individual’s well-being has been at the center of lot of discussions across micro-blogging sites and various social media platforms ever since it commenced in December 2019. Users are voicing their opinions on several topics related to covid-19. Social distancing as prescribed by Government and Local Administration We all are aware that the Novel Corona virus has significantly affected our physical health; however the current social distancing norms are taking a toll on the psychological well-being of individuals. The research paper presents a two-phased analysis of most recent 2000 tweets related to mental health pulled out twice over a span of one month on 28 June 2020 and 28 July2020 respectively, thereby analyzing 4000 tweets in total. The second phase analysis was conducted exactly after a gap of one month to validate the results generated by the first analysis. The intention is to analyze to what extent people have discussed about mental health in the past few months based on the information disseminated on Twitter. Data was extracted using Twitter’s search application programming interface (API) and Python’s tweepy library. A predefined keyword like ‘mental health’ was given to find out if Covid-19 emerges as a reason for the same. Several natural language processing (NLP) techniques like tokenization, removing URL and stop words, stemming and lemmatization were used to pre-process the text data and make it ready for analysis. These collected tweets were analyzed using word frequencies of single and double words (unigram, bigram). A very unique feature of this analysis includes a network diagram that shows interconnections between the set of most common words used in to its and the connections (if any) are represented through links. Topic modeling technique in NLP visualizes the top concerns of tweeters through a word cloud. At present we have many methods to do topic modeling. In this paper we are using the Latent Dirichlet Allocation (LDA) method which is a probabilistic approach of modeling given by Prof David H.B in 2003. This model deals with distribution of topics to tweets and allocation of those topics to documents and words to topics. Finally a sentiment analysis is done using text mining techniques to analyze the sentiment of the tweets in the form of positive, negative and neutral.

Social Policy and State–Community Relationships: Storm over Forest Rights in India

Abstract: Social Policy is concerned with minimising poverty and inequality through redistribution of goods and services. In the twentieth century, after the Second World War, European parliamentary democracies enlarged its ambit by making social policy an important instrument to create equality setting the benchmark for other countries. For the new independent countries in the global South, such as India, social policy followed different trajectories. In the aftermath of independence, India relied on preventive instruments to address the effects of famine, de-industrialisation and high levels of deprivation. Despite achieving high economic growth and rapid poverty reduction in the following decades, its dependence on targeted poverty reduction programme has remained. Recently, there has been some attempt to replace these strategies by rights-based programmes supported by legal framework advocated by civil society groups. Through a case study of The Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006 (referred to as FRA 2006), this article analyses the successes and failures in realising the goal of linking welfare provisions with the ideas of social citizenship and democratic rights. The article finds widening gulf in the interests of state actors and local community arising from the compromised interpretation of the social justice vision enshrined in FRA.

An approach to forecast impact of Covid-19 using supervised machine learning model.

Abstract: The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

India's New Rights Agenda: Genesis, Promises, Risks

Abstract: Since 2004, India has introduced a series of progressive national bills that enact a right to new civic entitlements, ranging from information, work and education to forest conservation, food and basic public services. What explains the emergence of these laws? How are the rights conceived by these acts conceptualized, operationalized and pursued? What are the promises, challenges and risks–legal, political and economic–of enshrining socioeconomic entitlements as formal statutory rights? This paper engages these questions. In part one, I argue that three slow-burning processes since the 1980s, distinct yet related, catalyzed India’s new rights agenda: high socio-legal activism, rapid uneven development and the expanding popular foundations of its federal parliamentary democracy. Significantly, all three processes exposed the growing nexus between political corruption and socioeconomic inequality. Equally, however, each raised popular expectations for greater social justice that were only partly met. Part two of the paper evaluates India’s new rights agenda. The promise of these new laws is threefold: they breach the traditional division of civil, political and socioeconomic rights, devise innovative governance mechanisms that enable citizens to see the state, and provide fresh incentives for new political coalitions to emerge across state and society. Several risks exist, however. Official political resistance from above and below, the limited capacities of judicial actors, state bureaucracies and social forces, and the relatively narrow base of many of these new movements endanger the potential of these reforms. The paper concludes by considering several imperatives that India’s evolving rights movement must confront to realize its ambition.

The impact of COVID-19 on migrant women workers in India

Abstract: COVID-19 has emerged as a crisis that has impacted all spheres of human life. The pandemic has disproportionately impacted the world’s poor population in terms of livelihood and survival. India wit...

Locked up under lockdown: The COVID-19 pandemic and the migrant population.

Abstract: Almost all countries in the world have now enforced coronavirus-related travel restrictions and border shutdowns. Migrant populations in the world are now considered the worst victims of COVID-19, and the spreaders too. This article aims to investigate the impacts that COVID-19 has on population migration. Informal interviews were conducted with the respondents who were stranded in different parts of the world via Skype and WhatsApp between February and November 2020. COVID-19 poses a threat to the millions who were caught between home and their final destinations - and are now living in overcrowded refugee camps where the terms 'stay home' 'stay safe' and 'social distancing' carries very little meaning.

Toward a climate mobilities research agenda: Intersectionality, immobility, and policy responses

Abstract: Mobility is a key livelihood and risk management strategy, including in the context of climate change. The COVID-19 pandemic has reinforced long standing concerns that migrant populations remain largely overlooked in economic development, adaptation to climate change, and spatial planning. We synthesize evidence across multiple studies that confirms the overwhelming preponderance of in-country and short distance rather than international migration in climate change hotspots in Asia and Africa. The emerging findings highlight the critical importance of addressing immobility and the intersecting social determinants that influence who can move and who cannot in development policy. This evidence suggests a more focused climate mobilities research agenda that includes understanding multiple drivers of mobility and multi-directional movement; intersecting social factors that determine mobility for some and immobility for others; and the implications for mobility and immobility under climate change and the COVID-19 recovery.