Understanding the Cultural Crisis: A Web Scraping Analysis of COVID-19 Vaccine Perceptions and Media Patterns

doi:10.21203/rs.3.rs-4297475/v1

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Understanding the Cultural Crisis: A Web Scraping Analysis of COVID-19 Vaccine Perceptions and Media Patterns

https://doi.org/10.21203/rs.3.rs-4297475/v1

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Social media platforms and online news channels have become even more essential during the COVID-19 pandemic, as they play a crucial role in distributing news widely in our digital era. News coverage during the COVID-19 pandemic has seen a substantial increase in speed, with information being quickly spread through both traditional media channels and social media platforms. Consequently, a surge in multiple conspiracy theories has caused individuals from different countries to raise unease about the safety and effectiveness of vaccines that were developed within a relatively short timeframe. Some key subjects discussed include the effectiveness of vaccines, different cultural perspectives on individual rights and public health in relation to vaccination, religious concerns about vaccines, and the prevalent skepticism and lack of trust in vaccines among both citizens and global communities. Our use of Python scripts allowed us to create a versatile solution for extracting, organizing, and interpreting data from the digital realm. This enabled us to track breaking news stories, analyze trends, and monitor mandatory regulations. This study utilized Python and web scraping techniques to conduct sentiment analysis. It aimed to examine the correlation between news flow and vaccine hesitancy and refusal across 29 countries. The analysis involved analyzing vaccine-related news and key terms. Our findings shed light on how news influences individuals' decisions to refuse vaccinations.

Nevertheless, the sheer volume of information also concerns its precision and trustworthiness. Python scripting in web scraping has become increasingly crucial in navigating the vast online news coverage. By automating the process, these scripts enable users to stay well-informed, transparent, and proactive in an era where the dependability and significance of information are crucial.

Information Retrieval and Management

web scarping

cultural analytics

vaccine hesitancy

COVID-19

Information retrieval mandatory and compulsory vaccination

The World Health Organization (WHO) labeled “vaccine hesitancy” as a substantial peril to worldwide health in 2019 (WHO 2021). Different demographic groups have diverse types of vaccine acceptance and vaccine hesitancy. Different countries have introduced rules and regulations regarding vaccinations. Early in 2020, the world was quickly consumed by the COVID-19 pandemic, causing millions of confirmed cases and putting immense pressure on healthcare systems globally (USC 2021). Some of the procedures put in place by governments were to create restrictions by closing the economy, limiting the movement of people, creating social distancing, and implementing cleanliness Governments, industries, employers, and organizations all experienced numerous changes as a result (Blum and Talib 2006).

Vaccine hesitancy has become a central issue in halting the COVID-19 pandemic in global health (WHO, WHO Int 2024). This paper will present findings from the digital humanities and information science by analyzing news related to vaccine hesitancy, mandatory vaccination, and compulsory vaccination. This paper used Python scripts to extract the news from news journals, websites, blogs, articles, and research papers.

With the available news, blogs, and articles online, this paper presents vaccination hesitancy and each country's government rules and regulations. By presenting the news in these terms, it has highlighted the topics of mandatory vaccinations, vaccine hesitancy, and the implementation of compulsory rules.

This paper primarily examines how cultural analytics affect vaccination delays and the dissemination of vaccination-related news across various demographics and geographical areas. At the same time, this paper identifies the news from 29 countries. This paper will analyze the news flow and its effects on the pandemic, without addressing the factors influencing vaccine avoidance.

In this section, we will cover the data collection methodology, the sentiment analysis process, details about our data sources, and specific information about the datasets themselves. We systematically collect and analyze data from online sources using widely-used Python libraries.

2.1 Data Collection

In this paper, data was collected from different websites using Python. Before utilizing a Python script, a manual process was employed to create a dataset by manually identifying important attributes and objects from the website. At a later stage, the paper decided to utilize a Python script to create a fresh dataset automatically. It is used as a web scraping tool to gather data from Google News using Python. The dataset contains information on COVID-19 vaccinations and includes a title, main body, and publication date. Some keywords have also been used to extract data from the website. Information about vaccine hesitancy and the requirement of vaccinations in different countries was collected from websites using targeted keywords.

The attributes/data fields include negative title, neutral title, compound title, negative sentiment, neutral sentiment, positive sentiment, compound sentiment, ID, title, link, published date, main text, country, number of characters, mandatory flag, hesitancy flag, and compulsory flag.

2.1.1 Data Source

Our data mainly originated from various online sources, including news views, blogs, research papers, WHO, governmental regulations, organizational rules, country rules, and historical resources focused on the COVID-19 outbreak.

2.1.1 Datasets

Python extracted datasets covering various topics, such as COVID-19 vaccinations, culture, and humanities. The team collectively created this dataset from scratch after reaching an agreement. It provides detailed information on COVID-19 vaccine hesitancy, mandatory measures, and compulsion. Additionally, it aims to extract data on the positivity, negativity, and neutrality surrounding vaccination across different countries. News from 29 countries was gathered, and the cultural perspectives of each country are described below.

2.1.2 Tools Used

We utilized Python, Oracle Data Visualisation Desktop, R, Tableau, Power BI, and MS.

This section will focus on the finer details of using Python for news scraping. We explored how to conduct new searches, retrieve article text, apply data filtering techniques, and analyze cultural perspectives surrounding topics like vaccine hesitancy, mandatory vaccines, and compulsory vaccination measures.

3.1 News Scraping with Python

This section will describe the approach taken to gather data, provide more information about our data sources, and give specific details about the datasets. Our methodical approach involves utilizing well-known Python libraries like BeautifulSoup and Scrapy to gather and analyze data from online sources. Our news scraping activity with Python revolves around these three core activities.

News research

News text body retrieval

Articles filtering and dataset creation

The first step was to retrieve a list of articles related to the research topic from the internet. For this reason, we decided to use the popular Google News package from the PyPI repository, which allows for quick queries on the Google News webpage by keywords, publication date, language, and region.

The query output is a JSON file containing information about matching results, such as the article title, the language, the publication date, and the web link. Our news search included the following terms.

{"title": "From cowpox to mumps: people have always had a problem with vaccination - The Conversation", "title_detail": {"type": "text/plain", "language": null, "base": "", "value": "From cowpox to mumps: people have always had a problem with vaccination - The Conversation"},

"published": "Wed, 19 Feb 2020 08:00:00 GMT", "published_parsed": [2020, 2, 19, 8, 0, 0, 2, 50, 0]

As we tested the package, we discovered that the number of search results was capped at up to 100 articles per query. For this reason, we decided to compute multiple queries by arranging different keywords, countries, and publishing dates. Although the query has a country parameter, we discovered that setting this to a specific country does not guarantee that search results are from that specific country; for this reason, we decided to add the country name in the research query directly. To streamline the analysis, we decided to stick with English and refrain from using local languages.

For the research query, we adopted the following skeleton:

{'vaccine covid {kword} {cname} after:{start_date} before:{end_date}'

where:

kword is a sentence between: [hesitancy, mandatory,compulsory]

cname is a country name among: [Austria, Belgium (BE), Bulgaria, Croatia, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany (DE), Greece, Hungary, Ireland, Italy (IT), Latvia, Lithuania, Luxembourg, Malta, Netherlands(NL), Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, UK, USA]

start_date, end_date are date ranges from November-2019 to June-2022 by month (32 months)

3.1.1 Result

To obtain all possible combinations, we have a total of 3x29x32 = 2784 queries.

Out of the 66462 articles we extracted, only 24001 (36%) were unique due to repetition across queries.

Retrieving the article text body can be a tricky task. Many approaches rely on web scraping techniques that access the web page source code for extracting text information. The main challenge with web scraping is that it requires a specific implementation per website, and sometimes, the desired text can be embedded in more complex data structures or objects such as JavaScript.

For this reason, we decided to rely on News-Please, another popular Python library, to handle the article's body retrieval.

The library successfully downloaded the text of 99% (22036 out of 24001) articles.

3.2 Data Filtering

We noticed that in some articles there were issues related to the downloaded text. During the data filtering process, we encountered the following notification :(not available anymore, news realise not able to automatically download it) Why did this happen? Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information, you can review our Terms of Service and Cookie Policy.

For this reason, we filtered out every article containing words like Java, JavaScript, cookie, and browser.

The filtering process resulted in 887 samples being removed, reducing the total number of samples to 21,149, which accounts for a 96% decrease.

Here is a typical example of our text-processing activity.

newline \n removal
to lowercase body and title
remove of special characters

We removed exceptionally long articles, surpassing the 90th percentile in word count and character count. The frequency of characters and words per article is visualized in Figs. 1 and 2.

3.2.1 Results

By applying this filter, the remaining articles are now 18838 (78% less).Our search results found that some articles are unrelated to the coronavirus (e.g., articles on other topics containing ads or quick news about the pandemic). Filtering by article content: name and body text must contain at least one vaccine, COVID-19, and the coronavirus.

While the filter successfully eliminated outliers, it significantly affected the dataset size, leaving only 10,563 articles (48%).

4.1 Cultural Perspectives

The cultural perspective, as defined by AlleyDog (AlleyDog, 2022), is the lens through which individuals view a situation or concept shaped by their native environment and social influence. This paper highlights the three main cultural perspectives of humans in the 29 countries where this analysis is done. According to the College of Physicians of Philadelphia (Philadelphia 2024), cultural perspectives are stated as follows:

The article "Individual Rights and Public Health Stances Toward Vaccination" by Parmet, Goodman, and Farber (2005) explores the relationship between individual rights and public health regarding vaccination.
The article "Various Religious Perspectives and Objections to Vaccines" by Pelčić et al. (2016) discusses different religious viewpoints and their objections to vaccines.
The issue of skepticism and mistrust towards vaccines is prevalent among citizens from diverse cultures and communities worldwide.

However, according to apa.org (2022), cultural perspectives are also recognized as a psychological framework that examines the causes and effects of behavior, with a particular emphasis on cross-cultural differences. Cultural contents such as religion, symbols, norms, ethics, appearances, communication, historical events, entertainment, food, clothing, governmental institutions, housing and type of agriculture, kind of physical environment, and information are not going to be covered in depth in this analysis (Philadelphia 2024). This paper will mainly focus on the cultural perspectives of vaccination within various countries.

4.1.1 Background History

From the start of the pandemic, different demographics have experienced diverse types of lockdown regulations and rules to halt vaccination spread. In 2006, Blum and Talib (Blum & Talib, 2006) examined the ancient custom of separation and quarantine as critical preventive measures with significant implications for civil liberties. Government regulations and rules affect the cultural elements of different demographics. One of their instruments is introducing a roadmap to the population, where countries use the roadmap out of the COVID-19 lockdown. Vaccination deployment programs in different countries were different. Even though the innovation of COVID-19 vaccinations is not in its 4th stage, the evidence shows vaccines are sufficiently effective in reducing hospitalization and deaths (Office 2021). This project will highlight the cultural perspective of vaccinations across different countries. At the same time, the various lockdown measures, such as introducing lockdown at a national level, closing educational institutes, restrictions on business and activities, reducing social contacts, travel restrictions, and restrictions on event organizing, were the key elements that affected the society norm. One anticipated finding was that even though vaccination can reduce hospitalization and deaths, the population of the various demographics was not fully participating in the vaccination program. In this case, the government introduced a measurement called vaccination, which is mandatory and compulsory for the inhabitants of the countries. Therefore, this project decided to analyze news articles related to vaccine hesitancy.

4.2 Vaccine Hesitancy

In the history of vaccination development, vaccination has been considered a cost-effective way to avoid diseases. Nonetheless, in countries experiencing high case numbers, they implement new guidelines and protocols to mitigate the spread of the virus both domestically and internationally (Nuwarda et al., 2022).

As stated by the ECDC, which stands for the European Centre for Disease Prevention and Control, vaccine hesitancy is defined as the unwillingness or resistance to receive vaccines, even when they are accessible in different countries (ECDC, 2022). This paper aims to compare and highlight the differences in vaccine hesitancy among various demographic groups in different countries, where vaccination is either mandatory or compulsory. The research will be conducted using accessible online resources and Python programming, as outlined in section five of this paper.

The news title is analyzed and filtered using some keywords. The extracted news also has a long main text, which is extended. The count of characters used in each title and main text is also displayed on the paper. This paper has highlighted the related news type between the start, during the pandemic, and the post-COVID situation in the different demographics.

4.2.1 Mandatory vaccination

This keyword refers to whether the vaccination was required by law, rule, or mandate (Collins, 2024). The word mandatory indicates that vaccination is binding on individuals within the country. For example, healthcare workers in the UK must be vaccinated for COVID-19. In this instance, the law requires vaccination; those who refuse to get vaccinated risk fines, travel restrictions, other penalties, and difficulties finding employment.

4.2.2 Compulsory Vaccination

The description of mandatory, compulsory Vaccination refers to the individual who must take the Vaccination because it is a rule or a law in the country (Collins, 2024). For example, Vaccination was compulsory for Italian healthcare workers and educational staff. In this case, Vaccination was not optional for the healthcare workers or governmental staff. Healthcare workers could perform their tasks with Vaccination and face some challenges.

This paper used Python to analyze the news related to COVID-19 within 29 countries. The title and main text of the news were analyzed to cover the sentiment hidden within the vaccination news across the different demographics. About 19 thousand texts and contents are analyzed in the news from early 2000 until June 2022. This text is mainly represented by country. Some news titles and primary texts do not have a correspondent country. This paper will describe the compulsory COVID-19 vaccination across different countries, the delay of vaccination acceptance (hesitancy) across different demographics, and news about the vaccination mandatory across different countries. In addition, this paper will display the news sentiment, such as positive, negative, and neutral.

The distribution of news/articles across countries is identified using the three keywords in Fig. 3: false means there is no compulsory vaccination, no vaccine hesitancy, and no mandatory vaccination, whereas true represents the vaccine compulsory, vaccine hesitancy, and vaccine mandatory.

5.1 Sentiment Analysis of the compulsory vaccination across date and time.

As mentioned in the introduction section, this paper has extracted the news articles where the count of compulsory vaccination and the count of vaccination is not compulsory. Figure 4 illustrates the initial findings of this project were to identify the news related to compulsory vaccinations across different countries

5.2 Results

The hypothesis is that the news about mandatory vaccinations will be tallied from the pandemic's beginning until June 2022. The count of vaccination where it was not compulsory and compulsory was almost the same during the start of the pandemic; this means that starting from January 2020 until July 2020, the news was not displaying information about vaccination is not compulsory. At the end of 2020, there was an increase in news about making vaccination mandatory across the countries. Many news articles were published between January 2021 and January 2022, highlighting the optional nature of vaccination. At the same time, the news of compulsory vaccination was flat, except for a high peak at the beginning of July 2021 and the end of 2021. Where the number of COVID cases in the countries is high, there are many articles about the compulsory COVID-19 vaccination. To minimize the chances of severe COVID-19 infection and hospitalization, various countries, organizations, and employers are disseminating information regarding COVID-19 vaccinations (Longo et al., 2020).

The following chart displays the count of COVID-19 vaccination compulsory articles and contents across different countries. The highest number of articles and texts in the news about vaccination being not compulsory were in the UK (2006 articles) and the USA (1955 articles). The exact dataset also has some information or articles where the country is not mentioned. At the same time, COVID-19 vaccination was compulsory in Austria with 214 articles, in France with 329 articles, in Germany with 299 articles, in Italy with 249 articles, in the UK with 401 articles, and in the USA with 316 articles. The count of optional and mandatory news flow by country is depicted in Fig. 5.

5.3 Sentiment Analysis of the vaccine hesitancy across time

The most important clinically relevant finding was that vaccination could reduce hospitalization and deaths. The following chart highlights articles on high vaccination hesitancy from the start of January 2020. The number of articles with no vaccine hesitancy was higher until July 2020. The dataset shows a high number of articles related to no vaccine hesitancy from January 2021 until June 2022.

The vaccine hesitancy across different countries was also displayed on the following chart. Surprisingly, the news related to the UK and USA was high. Surprisingly, the news of vaccine hesitancy was noted in this condition as a flat. Contrary to expectations, this project did not find significant differences in the news between the eastern part of Europe. Countries such as Austria, Italy, France, Germany, the USA, and the UK have more news related to vaccine hesitancy. The chart below describes the count of vaccine hesitancy articles and the count of no vaccine hesitancy by country.

5.4 Sentiment Analysis of the Mandatory Vaccination across countries

News related to the way the government, organizations, and employers compel the population to get vaccinations is presented in this section. The current study's findings from the news articles are consistent across the different countries, except in the UK and USA. This project is encouraged to compare the results of the mandatory vaccination to the compulsory one. The findings observed in this section mirror those in the compulsory one. For example, the news about the vaccination being mandatory in Austria is higher than the news that the vaccination is not mandatory, which means 385 articles > 245 articles. There are some similarities between the two news articles related to mandatory vaccination in Finland, Denmark, and Slovakia. As mentioned above, there is an observation that a high amount of news is related to vaccination being optional within the UK and USA. The illustration in Fig. 6 showcases the news flow related to mandatory and not mandatory vaccination across countries.

The following chart describes the flow of news articles where vaccination was mandatory or not mandatory in different countries. Regardless of the specific country, this paper identified a time when vaccination was not mentioned as mandatory at the beginning of January 2020. The observation from the analyses shows there was a flat curve from January 2020 until December 2020. It was also observed that there was a pick in the records where vaccination is mandatory for all, with a record of eighty-two articles at one point.

5.5 Sentiment Analysis of the negative articles across countries

The number of negative articles in the news is depicted in the following visualization, categorized by country. In Fig. 8, we can observe a peak in negative news coverage, particularly in Austria, USA, and the UK.

5.6 Sentiment Analysis of the positive, negative, and neutral articles by country.

Also included in this paper is a table that displays positive, negative, and neutral news articles from different countries. In Austria, the government considers not getting vaccinated a crime. In Fig. 9, you can see the breakdown of sentiments (positive, negative, and neutral) in Austria through sentiment analysis.

5.6.1 Analysing the frequency of news flow using word cloud

Using a word cloud, this project showcases the frequency of news articles from Austria. The news that appeared most frequently were visualized through a word cloud. The Austrian news flow was depicted in Fig. 10 using a word cloud.

The above word cloud shows a strong bias towards news representation in Austria, possibly because Austria was the first country to impose strict penalties for vaccine avoidance or refusal.

In the following graph, we will present the neutral articles on covid-19 vaccination, sorted by publication date and geographical distribution.

In Fig. 12 below, you can also see the count of news articles by country regarding vaccine hesitancy, compulsory and mandatory measures.

This project has argued that the interdisciplinary relationship between cultural perspectives and news related to vaccination hesitancy, mandatory and compulsory, has some positive, negative, and neutral ones. A key finding from his project is that sentiment analysis has successfully analyzed vaccine-related news in various countries. Extracting data and analyzing news using Python might make it relevant to perform cultural analytics and semantic analysis. The present project makes several noteworthy contributions to answer the effect of news flow across society during and after the lockdown. The semantic analysis of vaccination uptake across different societies shows the crucial strength of the Python tool in generating a dataset with flow news. The countries' rules, regulations, and measurements across different demographics show that a diverse cultural perspective exists. While this project did not confirm vaccination uptake and vaccine hesitancy at a granular level, the effect of news flow across the different countries can be seen as negative, positive, and neutral. This project also confirms that it is possible to perform semantic analysis for the content of various titles and primary texts in the news in a specific timeline.

The versatile toolkit provided by Python web scraping can be a valuable asset for healthcare industry professionals and decision-makers, particularly during times of crisis such as the COVID-19 pandemic. It facilitates the extraction of information or gathering data in real-time from different sources, enabling the monitoring of trends, public sentiment, and the availability of resources. It also helps make informed decisions, allocate resources, and detect outbreaks early. Ultimately, it empowers professionals to enhance communication strategies, optimize responses, and achieve better public health outcomes.

Ethics Approval and Consent to Participate

All procedures performed in studies involving human participants were by the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This paper has got ethical approval from the EuCARE project lead. All human contributors involved in the research study provided knowledgeable consent prior to their participation. The aim of the study was made known, the methods involved, advantages and likely dangers, and their freedom as participants. Participants had been guaranteed that their involvement was deliberate, and they reserved the option to pull out from the study whenever without punishment.

Conflict of interest statement

The authors are 3rd-year Ph.D. candidates at the Sapienza University of Rome. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest." The authors listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Consent for publication

We have evaluated and approved the final version of this manuscript, and thus give my permission for publication.

Availability of data and materials

This study has not used personally identifiable information (PII). The dataset used in this study is available upon request. Additionally, the Github link for this project is accessible.

Funding

This paper does not require financing and has not requested any, either.

Authors’ contribution

The presented idea was conceived by Yacob Gebretensae. The theory's development and analysis were a collaborative effort by all authors, who utilized Phyton. All authors confirmed the validity of the analytical methods and sentiment analysis. The results were thoroughly discussed by all authors, who also made significant contributions to the final manuscript.

Acknowledgment

We are incredibly grateful to Dr. Giorgio Barnabò for his invaluable contribution to this project and extend our sincerest appreciation to him. His knowledge and advice have played a crucial role in molding its triumph.

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AlleyDog. 2022. AlleyDog . 13 December . Accessed Feburary 13, 2024. https://www.alleydog.com/glossary/definition.php?term=Cultural+Perspective.
AN, N., Buzzi, Bedreag B, Papurica O, Rogobete M, Sandesc A, Sorescu D, Baditoiu T, Musuroi L, Vlad C, and Licker, M D. 2022. “Bacterial and Fungal Superinfections in COVID-19 Patients Hospitalized in an Intensive Care Unit from Timișoara, Romania.” Infection and Drug Resistance 7001-7014.
Blum, John D, and Norchaya Talib. 2006. “Balancing individual rights versus collective good in public health enforcement.” Medicine and Law 273-283.
Brusselen, Daan Van, Ali Heshima Dubois, Lucien Kandundao Bindu, Zakari Moluh, Yvonne Nzomukunda, and Laurens Liesenborghs. 2024. “vaccination campaign hesitancy drives measles epidemics in conflict-torn eastern DR Congo.” Confilict and Health 1-3.
Collins. 2024. Collins Dictionary . 23 Feburary . Accessed March 20, 2024. https://www.collinsdictionary.com/dictionary/english/compulsory-vaccination.
Fhamborg. 2022. “Github: news-please: A Generic News Crawler and Extractor,.” Github . 20 June . Accessed June 2022, 2022. https://github.com/fhamborg/news-please.
HurinHu. 2022. “github Google News .” Github . 20 June . Accessed June 23, 2022. https://github.com/Iceloof/GoogleNews.
Longo, Miriam, Paola Caruso, Maria Ida Maiorino, Giuseppe Bellastella, Dario Giugliano, and Katherine Esposito. 2020. “Treating type 2 diabetes in COVID-19 patients: The potential benefits of injective therapies. .” Cardiovascular Diabetology.
Luca, Cătălina M., Doina Azoicăi, Ioana Harja Alexa, Andrei Vâță, Natalia Cucoș, Andreea Pascariu, and Ioana M. Hunea. 2020. “Cultural Perspectives on Vaccination - AN Ethical Dilemma? .” Journal of Intercultural Management and Ethics 1-33.
Nuwarda, Rina Fajri, Iqbal Ramzan, Lynn Weekes, and Veysel Kayser. 2022. “Vaccine Hesitancy: Contemporary Issues and Historical Background.” Vaccines (Basel) 10.
Office, Cabinet. 2021. COVID-19 Response - Spring 2021 (Roadmap). London , 22 Feburary .
Parmet, Wendy E, Richard A Goodman, and Amy Farber. 2005. “Individual rights versus the public's health--100 years after Jacobson v. Massachusetts.” N Engl J Med 652-653.
Pelčić, Gordana, Silvana Karačić, Galina L. Mikirtichan, Olga I. Kubar, Frank J. Leavitt, Michael Cheng-tek Tai, Naoki Morishita, Suzana Vuletić, and Luka Tomašević. 2016. “Religious exception for vaccination or religious excuses for avoiding vaccination.” Croat Med J 516-521.
Philadelphia, The College of Physicians of. 2024. Individual versus Public Health Stances. Philadelphia, 20 Feburary .
USC. 2021. calendar.usc.edu. 4 Feburary . Accessed December 12, 2023. https://calendar.usc.edu/event/town_hall_talk_overcoming_covid-19_vaccine_hesitancy.
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Python Script and GitHub address for the project Link:

https://github.com/bottaluscio/cultural-analytics

Main Dataset (Big Data)

data/pygooglenews/metadata.csv

The authors declare no competing interests.

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Understanding the Cultural Crisis: A Web Scraping Analysis of COVID-19 Vaccine Perceptions and Media Patterns

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Abstract

Figures

1 Introduction

2 Methodology

2.1 Data Collection

2.1.1 Data Source

2.1.1 Datasets

2.1.2 Tools Used

3 Discussion

3.1 News Scraping with Python

3.1.1 Result

3.2 Data Filtering

3.2.1 Results

4 Results

4.1 Cultural Perspectives

4.1.1 Background History

4.2 Vaccine Hesitancy

4.2.1 Mandatory vaccination

4.2.2 Compulsory Vaccination

5 Sentiment analysis using Python

5.1 Sentiment Analysis of the compulsory vaccination across date and time.

5.2 Results

5.3 Sentiment Analysis of the vaccine hesitancy across time

5.4 Sentiment Analysis of the Mandatory Vaccination across countries

5.5 Sentiment Analysis of the negative articles across countries

5.6 Sentiment Analysis of the positive, negative, and neutral articles by country.

5.6.1 Analysing the frequency of news flow using word cloud

6 Conclusion

Declarations

References

Appendix

Additional Declarations

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