Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5639

Keywords:

Vaccine myths, Sentiment analysis, Reddit, word cloud, social media analysis

Abstract

INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children.

OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users.

METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go.  All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated.

RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment.

CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.

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Published

04-04-2024

How to Cite

1.
Bhatia TK, Rathi S, Singh TP, Naha B. Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 4 [cited 2024 May 20];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5639