The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling

Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users' perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level. Copyright © The Author(s) 2023.

iNCOVACC COVID-19 vaccine: A Twitter based Social Media Analysis Using Natural Language Processing, Sentiment Analysis, and Topic Modelling

Most, if not all, the vaccine candidates designed to counteract COVID-19 due to SARS-CoV-2 infection require parenteral administration. Mucosal immunity established by vaccination could significantly contribute to containing the SARS-CoV-2 pandemic, which is spread by infected respiratory secretions. The world has been impacted on many fronts by the COVID-19 pandemic since early 2020 and has yet to recover entirely from the impact of the crisis. In late 2022 and early 2023, China experienced a new surge of COVID-19 outbreaks, mainly in the country's northeastern region. With the threat of new variants like XBB 1.5 and BF.7, India might experience a similar COVID-19 surge as China and needs to be prepared to avoid destruction again. An intranasal vaccine can elicit multiple immunological responses, including IgG neutralization, mucosal IgA production, and T-cell responses. In order to prevent further infection and the spread of COVID-19, local immune responses in the nasal mucosa are required. iNCOVACC is a recombinant vaccine vectored by an adenovirus that contains a SARS-CoV-2 spike protein that has been pre-fusion stabilized. This vaccine candidate has shown promise in both early and late-stage clinical trials. iNCOVACC has been designed for intranasal administration via nasal drops. The nasal delivery system was created to reduce expenses for those living in poor and moderate-income. © 2023, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.

Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts using Natural Language Processing, to Understand People's Perspectives Regarding COVID-19 Omicron Subvariants XBB.1.5 and BF.7

Concerns about an increase in cases during the COVID-19 pandemic have been heightened by the emergence of a new Omicron subvariant XBB.1.5 that joined the previously reported BF.7 as a source of public health concern. COVID-19 cases have been on the rise intermittently throughout the ongoing pandemic, likely because of the continuous introduction of SARS-CoV-2 subtypes. The present study analyzed the Indian citizen's perceptions of the latest covid variants XBB.1.5 and BF.7 using the natural language processing technique, especially topic modeling and sentiment analysis. The tweets posted by Indian citizens regarding this issue were analyzed and used for this study. Government authorities, policymakers, and healthcare officials will be better able to implement the necessary policy effectively to tackle the XBB 1.5 and BF.7 crises if they are aware of the people's sentiments and concerns about the crisis. A total of 8,54,312 tweets have been used for this study. Our sentiment analysis study has revealed that out of those 8,54,312 tweets, the highest number of tweets (n = 3,19,512 tweets (37.3%)) about COVID variants XBB.1.5 and BF.7 had neutral sentiments, 3,16,951 tweets (37.1%) showed positive sentiments and 2,17,849 tweets (25.4%) had negative sentiments. Fear of the future and concerns about the immunity of the vaccines are of prime concerns to tackle the ongoing pandemic. Copyright © The Author(s) 2023.

SARS-CoV-2 emerging Omicron subvariants with a special focus on BF.7 and XBB.1.5 recently posing fears of rising cases amid ongoing COVID-19 pandemic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron versions have been the sole one circulating for quite some time. Subvariants BA.1, BA.2, BA.3, BA.4, and BA.5 of the Omicron emerged over time and through mutation, with BA.1 responsible for the most severe global pandemic between December 2021 and January 2022. Other Omicron subvariants such as BQ.1, BQ.1.1, BA.4.6, BF.7, BA.2.75.2, XBB.1 appeared recently and could cause a new wave of increased cases amid the ongoing COVID-19 pandemic. There is evidence that certain Omicron subvariants have increased transmissibility, extra spike mutations, and ability to overcome protective effects of COVID-19 neutralizing antibodies through immunological evasion. In recent months, the Omicron BF.7 subvariant has been in the news due to its spread in China and a small number of other countries, raising concerns about a possible rebound in COVID-19 cases. More recently, the Omicron XBB.1.5 subvariant has captured international attention due to an increase in cases in the United States. As a highly transmissible sublineage of Omicron BA.5, as well as having a shorter incubation time and the potential to reinfect or infect immune population, BF.7 has stronger infection ability. It appears that the regional immunological landscape is affected by the amount and timing of previous Omicron waves, as well as the COVID-19 vaccination coverage, which in turn determines whether the increased immune escape of BF.7 and XBB.1.5 subvariants is sufficient to drive new infection waves. Expanding our understanding of the transmission and efficacy of vaccines, immunotherapeutics, and antiviral drugs against newly emerging Omicron subvariants and lineages, as well as bolstering genomic facilities for tracking their spread and maintaining a constant vigilance, and shedding more light on their evolution and mutational events, would help in the development of effective mitigation strategies. Importantly, reducing the occurrence of mutations and recombination in the virus can be aided by bolstering One health approach and emphasizing its significance in combating zoonosis and reversal zoonosis linked with COVID-19. This article provides a brief overview on Omicron variant, its recently emerging lineages and subvairants with a special focus on BF.7 and XBB.1.5 as much more infectious and highly transmissible variations that may once again threaten a sharp increase in COVID-19 cases globally amid the currently ongoing pandemic, along with presenting salient mitigation measures. © 2022, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.

How optimistic do citizens feel about digital contact tracing? – Perspectives from developing countries

Purpose>Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing.Design/methodology/approach>For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing.Findings>The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing.Originality/value>After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.

Analyzing Indian general public's perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets.

BACKGROUND AND AIMS: Ever since COVID-19 was declared a pandemic by WHO in late March 2020, more and more people began to share their opinions online about the anxiety, stress, and trauma they suffered because of the pandemic. However, very few studies were conducted to analyze the general public's perception of what causes stress, anxiety, and trauma during COVID-19. This study focuses particularly on understanding Indian citizens. METHODS: By using Machine learning techniques, particularly Natural language processing, this study focuses on understanding the attitude of Indian citizens while discussing the anxiety, stress, and trauma created because of COVID-19 and the major reasons that cause it. We used Tweets as data for this study. We have used 840,000 tweets for this study. RESULTS: Our sentiment analysis study revealed the interesting fact that, even while discussing about the stress, anxiety, and trauma caused by COVID-19, most of the tweets were in neutral sentiments. Death and Lockdown caused by the COVID-19 were the two most important aspects that cause stress, anxiety, and Trauma among Indian citizens. CONCLUSION: It is important for policymakers and health professionals to understand common citizen's perspectives of what causes them stress, anxiety, and trauma to formulate policies and treat the patients. Our study shows that Indian citizens use social media to share their opinions about COVID-19 and as a coping mechanism in unprecedented time.

Analyzing the attitude of Indian citizens towards COVID-19 vaccine - A text analytics study.

BACKGROUND AND AIMS: The government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media. METHODS: For this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine. RESULTS: Our results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine. CONCLUSION: With the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.

An analysis of attitude of general public toward COVID-19 crises – sentimental analysis and a topic modeling study

Purpose: It has been eight months into the global pandemic health crises COVID-19, yet the severity of the crises is just getting worse in many parts of the world. At this stage, it is essential to understand and observe the general attitude of the public toward COVID crises and the major concerns the public has voiced out and how it varies across months. Understanding the impact that the COVID-19 crises have created also helps policymakers and health-care organizations access the primary steps that need to be taken for the welfare of the community. The purpose of this study is to understand the general public's response towards COVID-19 crises and the major issues that concerns them. Design/methodology/approach: For the analysis, data were collected from Twitter. Tweets regarding COVID-19 crises were collected from February 1, 2020, to June 27, 2020. In all, 433,195 tweets were used for this study. Natural language processing (NLP), which is a part of Machine learning, was used for this study. NLP was used to track the changes in the general public's sentiment toward COVID-19 crises and LDA was used to understand the issues that shape the general public's sentiments the crises time. Using Python library Wordcloud, the authors further derived how the primary concerns regarding COVID crises various from February to June of the year 2020. Findings: This study was conducted in two parts. Study 1 results showed that the attitude of the general public toward COVID crises was reasonably neutral at the beginning of the crises (Month of February). As the crises become severe, the sentiments toward COVID increasingly become negative yet a considerable percentage of neutral sentiments existed even at the peak time of the crises. Study 2 finds out that issues including the severity of the disease, Precautionary measures need to be taken, and Personal issues like unemployment and traveling during the pandemic time were identified as the public's primary concerns. Originality/value: The research adds value to the literature on understanding the major issues and concerns, the public voices out about the current ongoing pandemic. To the best of the authors’ knowledge, this is the first study with an extended period of timeframe (Five months). In this research, the authors have collected data till June for analysis that makes the results and findings more relevant to the current time. © 2020, Emerald Publishing Limited.

General public's attitude toward governments implementing digital contact tracing to curb COVID-19-a study based on natural language processing

Purpose Governments worldwide are taking various measures to prevent the spreading of COVID virus. One such effort is digital contact tracing. However, the aspect of digital contact tracing was met with criticism, as many critics view this as an attempt of the government to control people and a fundamental breach of privacy. Using machine learning techniques, this study aims to deal with understanding the general public's emotions toward contact tracing and determining whether there is a change in the attitude of the general public toward digital contact tracing in various months of crises. This study also analyzes the significant concerns voiced out by the general public regarding digital contact tracing. Design/methodology/approach For the analysis, data were collected from Reddit. Reddit posts discussing the digital contact tracing during COVID-19 crises were collected from February 2020 to July 2020. A total of 5,025 original Reddit posts were used for this study. Natural language processing, which is a part of machine learning, was used for this study to understand the sentiments of the general public about contact tracing. Latent Dirichlet allocation was used to understand the significant issues voiced out by the general public while discussing contact tracing. Findings This study was conducted in two parts. Study 1 results show that the percentage of general public viewing the aspect of contact tracing positively had not changed throughout the time period of Data frame (March 2020 to July 2020). However, compared to the initial month of the crises, the later months saw a considerable increase in negative sentiments and a decrease in neutral sentiments regarding the digital contact tracing. Study 2 finds out the significant issues public voices out in their negative sentiments are a violation of privacy, fear of safety and lack of trust in government. Originality/value Although numerous studies were conducted on how to implement contact tracing effectively, to the best of the authors' knowledge, this is the first study conducted with an objective of understanding the general public's perception of contact tracing.