Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Ali S. Abed Al Sailawi; Mohammad Reza Kangavari; Ali S. Abed Al Sailawi; Mohammad Reza Kangavari

doi:10.3934/publichealth.2024018

AIMS Public Health

2024, Volume 11, Issue 2: 349-378. doi: 10.3934/publichealth.2024018

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Technical note

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Ali S. Abed Al Sailawi ^{1,2
,
,},
Mohammad Reza Kangavari ^{1
,
,}

1.
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
2.
College of Law, University of Misan, Amarah, Iraq

Received: 11 January 2024 Revised: 27 February 2024 Accepted: 12 March 2024 Published: 18 March 2024

This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models–BiLSTM, FFNN, and CNN – highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.
- AI,
- Twitter feeds,
- COVID-19,
- crisis response,
- WHO,
- vaccination,
- decision support system,
- public sentiment,
- real-time data analysis
Citation: Ali S. Abed Al Sailawi, Mohammad Reza Kangavari. Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network[J]. AIMS Public Health, 2024, 11(2): 349-378. doi: 10.3934/publichealth.2024018

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Abstract

This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models–BiLSTM, FFNN, and CNN – highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.

Acknowledgments

We extend our gratitude to the faulty members for their assistance in providing the supplementary data on the survey. We also appreciate their valuable contributions to the discussions related to this study.

Conflicts of Interest

The authors declare no conflict of interest.

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