Covid-19 Vaccine Side Effect Analysis by Leveraging Social Media
Focusing on Connectivity and Cluster Characteristics of Vaccine Side Effects
Bibliographic Data
| ID | 22108558 |
|---|---|
| Authors | Sunguk Yun (0009-0007-8912-7436, Kongju National University), Jaekyun Jeong (Kongju National University), Jungeun Kim (0000-0002-6474-5058, Kongju National University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6487-6500 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3392341 |
| OpenAlex | W4398150961 |
| Language | EN |
| References cited | 26 |
COVID-19, a highly contagious global epidemic, has prompted governments to actively recommend vaccination as a crucial measure to overcome its impact. However, vaccine hesitancy remains a significant challenge, stemming from concerns related to rapid vaccine development, streamlined clinical trials, misinformation, and potential side effects. To address these concerns, an in-depth understanding of COVID-19 vaccine side effects is paramount. This article aims to analyze COVID-19 vaccine side effects using machine learning applied to Twitter, a representative social media platform. Thorough experiments show that we can not only detect officially known COVID-19 vaccine side effects, such as pain and headache but also identify previously unknown COVID-19 vaccine side effects like myocarditis and thrombosis. More importantly, we show that connectivity analysis and cluster analysis can provide a more detailed understanding of vaccine side effects, including differences from conventional text-mining analysis results. This article has the potential to alleviate public anxiety by discovering and analyzing vaccine side effects through social media data analysis. In addition, the proposed method is more important because it can be applied not only to COVID-19 vaccines but also to other side effects related to other medications
Computer network · Social media · World Wide Web · Computer Science · Medicine · Misinformation and Its Impacts · Pharmacovigilance and Adverse Drug Reactions · Vaccine Coverage and Hesitancy · Virology
The anatomy of a large-scale hypertextual Web search engine
Fast unfolding of communities in large networks
A Deep Learning Approach for Semantic Analysis of Covid-19-Related Stigma on Social Media
Changes of the Public Attitudes of China to Domestic Covid-19 Vaccination After the Vaccines Were Approved
Characterizing the Propagation of Situational Information in Social Media During Covid-19 Epidemic
Covidsenti
Predicting Infectious Disease Using Deep Learning and Big Data
Developing a Covid-19 Crisis Management Strategy Using News Media and Social Media in Big Data Analytics
From translations to problematic networks
| Citation velocity | historical |
|---|---|
| Highly cited | No |