Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

Tarandeep Kaur Bhatia; Samagya Rathi; Thipendra P Singh; Biswayan Naha

doi:10.4108/eetpht.10.5639

Authors

Tarandeep Kaur Bhatia University of Petroleum and Energy Studies
Samagya Rathi University of Petroleum and Energy Studies
Thipendra P Singh Bennett University
Biswayan Naha University of Petroleum and Energy Studies

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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References

Ansari, Md Tarique Jamal, and Naseem Ahmad Khan. "Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content." Electronic Journal of General Medicine 18, no. 6 (2021). DOI: https://doi.org/10.29333/ejgm/11316

Yin, Hui, Xiangyu Song, Shuiqiao Yang, and Jianxin Li. "Sentiment analysis and topic modeling for COVID-19 vaccine discussions." World Wide Web 25, no. 3 (2022): 1067-1083.

Mohan, Sumit, Anil Kumar Solanki, Harish Kumar Taluja, and Anuj Singh. "Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach." Computers in Biology and Medicine 144 (2022): 105354. DOI: https://doi.org/10.1016/j.compbiomed.2022.105354

Ali, GG Md Nawaz, Md Mokhlesur Rahman, Md Amjad Hossain, Md Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, and Jim Samuel. "Public perceptions of COVID-19 vaccines: Policy implications from US spatiotemporal sentiment analytics." In Healthcare, vol. 9, no. 9, p. 1110. MDPI, 2021.

Hananto, Andhika Rafi, Silvia Anggun Rahayu, and Taqwa Hariguna. "COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia." International Journal of Informatics and Information Systems 5, no. 1 (2022): 56-68. DOI: https://doi.org/10.47738/ijiis.v5i1.126

Ali, GG Md Nawaz, Md Mokhlesur Rahman, Md Amjad Hossain, Md Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, and Jim Samuel. "Public perceptions of COVID-19 vaccines: Policy implications from US spatiotemporal sentiment analytics." In Healthcare, vol. 9, no. 9, p. 1110. MDPI, 2021. DOI: https://doi.org/10.3390/healthcare9091110

Yadav, Ashima, and Dinesh Kumar Vishwakarma. "A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis." ACM Transactions on Internet Technology (TOIT) 22, no. 1 (2021): 1-30. DOI: https://doi.org/10.1145/3475867

Riyanto, Riyanto, and Abdul Azis. "Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia." Journal of Applied Data Sciences 2, no. 3 (2021): 102-108. DOI: https://doi.org/10.47738/jads.v2i3.40

Kwok, Stephen Wai Hang, Sai Kumar Vadde, and Guanjin Wang. "Tweet topics and sentiments relating to COVID-19 vaccination among Australian Twitter users: machine learning analysis." Journal of medical Internet research 23, no. 5 (2021): e26953. DOI: https://doi.org/10.2196/26953

Shofiya, Carol, and Samina Abidi. "Sentiment analysis on COVID-19-related social distancing in Canada using Twitter data." International Journal of Environmental Research and Public Health 18, no. 11 (2021): 5993. DOI: https://doi.org/10.3390/ijerph18115993

Eachempati, Prajwal, Praveen Ranjan Srivastava, and Prabin Kumar Panigrahi. "Sentiment analysis of COVID-19 pandemic on the stock market." American Business Review 24, no. 1 (2021): 8. DOI: https://doi.org/10.37625/abr.24.1.141-165

Mushtaq, Muhammad Faheem, Mian Muhammad Sadiq Fareed, Mubarak Almutairi, Saleem Ullah, Gulnaz Ahmed, and Kashif Munir. "Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques." Vaccines 10, no. 5 (2022): 661. DOI: https://doi.org/10.3390/vaccines10050661

Alabrah, Amerah, Husam M. Alawadh, Ofonime Dominic Okon, Talha Meraj, and Hafiz Tayyab Rauf. "Gulf countries’ citizens’ acceptance of COVID-19 vaccines—A machine learning approach." Mathematics 10, no. 3 (2022): 467. DOI: https://doi.org/10.3390/math10030467

Yin, Hui, Xiangyu Song, Shuiqiao Yang, and Jianxin Li. "Sentiment analysis and topic modeling for COVID-19 vaccine discussions." World Wide Web 25, no. 3 (2022): 1067-1083. DOI: https://doi.org/10.1007/s11280-022-01029-y

Xu, Han, Ruixin Liu, Ziling Luo, and Minghua Xu. "COVID-19 Vaccine Sensing: Sentiment Analysis and Subject Distillation from Twitter Data." Available at SSRN 4073419.

Sarirete, Akila. "Sentiment analysis tracking of COVID-19 vaccine through tweets." Journal of Ambient Intelligence and Humanized Computing (2022): 1-9. DOI: https://doi.org/10.1007/s12652-022-03805-0

Roy, Tamal Joyti, Md Ashiq Mahmood, and Aninda Mohanta. "An Efficient Approach to Validate COVID-19 Related Vaccine Myths Utilizing LDA Algorithm." (2022). DOI: https://doi.org/10.1145/3542954.3542963

Alamoodi, Abdullah Hussein, B. B. Zaidan, Maimonah Al-Masawa, Sahar M. Taresh, Sarah Noman, Ibraheem YY Ahmaro, Salem Garfan et al. "Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy." Computers in Biology and Medicine 139 (2021): 104957. DOI: https://doi.org/10.1016/j.compbiomed.2021.104957

Schmitz, Matheus, Goran Murić, and Keith Burghardt. "A Python Package to Detect Anti-Vaccine Users on Twitter." arXiv preprint arXiv:2110.11333 (2021).

Kummervold, Per E., Sam Martin, Sara Dada, Eliz Kilich, Chermain Denny, Pauline Paterson, and Heidi J. Larson. "Categorizing vaccine confidence with a transformer-based machine learning model: analysis of nuances of vaccine sentiment in Twitter discourse." JMIR medical informatics 9, no. 10 (2021): e29584. DOI: https://doi.org/10.2196/29584

Lyu, Joanne Chen, Eileen Le Han, and Garving K. Luli. "COVID-19 vaccine–related discussion on Twitter: topic modeling and sentiment analysis." Journal of medical Internet research 23, no. 6 (2021): e24435. DOI: https://doi.org/10.2196/24435

Agustiningsih, Kartikasari Kusuma, Ema Utami, and Hanif Al Fatta. "Sentiment Analysis of COVID-19 Vaccine on Twitter Social Media: Systematic Literature Review." In 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 121-126. IEEE, 2021.

Lopez Torres, Ivan. "Twitter-Vaccine Sentiment Analysis." Available at SSRN 3942987 (2021). DOI: https://doi.org/10.2139/ssrn.3942987

Klimiuk, Krzysztof, Agnieszka Czoska, Karolina Biernacka, and Łukasz Balwicki. "Vaccine misinformation on social media–topic-based content and sentiment analysis of Polish vaccine-deniers’ comments on Facebook." Human Vaccines & Immunotherapeutics 17, no. 7 (2021): 2026-2035.

Piedrahita-Valdés, Hilary, Diego Piedrahita-Castillo, Javier Bermejo-Higuera, Patricia Guillem-Saiz, Juan Ramón Bermejo-Higuera, Javier Guillem-Saiz, Juan Antonio Sicilia-Montalvo, and Francisco Machío-Regidor. "Vaccine hesitancy on social media: sentiment analysis from June 2011 to April 2019." Vaccines 9, no. 1 (2021): 28. DOI: https://doi.org/10.3390/vaccines9010028

Raghupathi, Viju, Jie Ren, and Wullianallur Raghupathi. "Studying public perception about vaccination: A sentiment analysis of tweets." International journal of environmental research and public health 17, no. 10 (2020): 3464. DOI: https://doi.org/10.3390/ijerph17103464

Porreca, Annamaria, Francesca Scozzari, and Marta Di Nicola. "Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination." BMC Public Health 20, no. 1 (2020): 1-9. DOI: https://doi.org/10.1186/s12889-020-8342-4

Jayasurya, Gutti Gowri, Sanjay Kumar, Binod Kumar Singh, and Vinay Kumar. "Analysis of Public Sentiment on COVID-19 Vaccination Using Twitter." IEEE Transactions on Computational Social Systems (2021). DOI: https://doi.org/10.1109/TCSS.2021.3122439

Hayawi, Kadhim, Sakib Shahriar, Mohamed Adel Serhani, Ikbal Taleb, and Sujith Samuel Mathew. "ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection." Public health 203 (2022): 23-30.

Kricorian, Katherine, Rachel Civen, and Ozlem Equils. "COVID-19 vaccine hesitancy: Misinformation and perceptions of vaccine safety." Human Vaccines & Immunotherapeutics 18, no. 1 (2022): 1950504. DOI: https://doi.org/10.1080/21645515.2021.1950504

Sarker, Puja, and Dibbendu Kumar Sarkar. "Sentiment Analysis of General Peoples Reaction About Covid19 Vaccination in Bangladesh Using Machine Learning Algorithm from Bengali Text Dataset." (2021).

Dumre, Rutvik, Kopal Sharma, and Karthik Konar. "Statistical and Sentimental Analysis on Vaccination against COVID-19 in India." In 2021 International Conference on Communication information and Computing Technology (ICCICT), pp. 1-6. IEEE, 2021.

Dumre, Rutvik, Kopal Sharma, and Karthik Konar. "Statistical and Sentimental Analysis on Vaccination against COVID-19 in India." In 2021 International Conference on Communication information and Computing Technology (ICCICT), pp. 1-6. IEEE, 2021. DOI: https://doi.org/10.1109/ICCICT50803.2021.9510179

Kartikasari Kusuma Agustiningsih, Ema Utami, Hanif Al Fatta. "Sentiment Analysis of COVID- 19 Vaccine on Twitter Social Media: Systematic Literature Review", 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2021 DOI: https://doi.org/10.1109/ICITISEE53823.2021.9655960

Britzolakis, Alexandros, Haridimos Kondylakis, and Nikolaos Papadakis. "AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter." Information 12, no. 8 (2021): 312. DOI: https://doi.org/10.3390/info12080312

Krzysztof Klimiuk, Agnieszka Czoska, Karolina Biernacka, Łukasz Balwicki. "Vaccine misinformation on social media – topic-based content and sentiment analysis of Polish vaccine-deniers’ comments on Facebook", Human Vaccines & Immunotherapeutics, 2021 DOI: https://doi.org/10.1080/21645515.2020.1850072

Kandasamy, Venkatachalam, Pavel Trojovský, Fadi Al Machot, Kyandoghere Kyamakya, Nebojsa Bacanin, Sameh Askar, and Mohamed Abouhawwash. "Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme." Sensors 21, no. 22 (2021): 7582. DOI: https://doi.org/10.3390/s21227582

Waheeb, Samer Abdulateef, Naseer Ahmed Khan, and Xuequn Shang. "Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets." Electronics 11, no. 5 (2022): 715. DOI: https://doi.org/10.3390/electronics11050715

K. Hayawi, S. Shahriar, M.A. Serhani, I. Taleb, S.S. Mathew. "ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection", Public Health, 2022 DOI: https://doi.org/10.1016/j.puhe.2021.11.022

Han Xu, Ruixin Liu, Ziling Luo, Minghua Xu, Bang Wang. "COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data", 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021 DOI: https://doi.org/10.1109/SMC52423.2021.9658764