Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data
Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 Aug...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Vaccines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/10/1/103 |
| _version_ | 1797489878942875648 |
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| author | Andrew T. Lian Jingcheng Du Lu Tang |
| author_facet | Andrew T. Lian Jingcheng Du Lu Tang |
| author_sort | Andrew T. Lian |
| collection | DOAJ |
| description | Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems. |
| first_indexed | 2024-03-10T00:22:54Z |
| format | Article |
| id | doaj.art-88f0e568bcfe4bae9b869ae117f589bf |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-10T00:22:54Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-88f0e568bcfe4bae9b869ae117f589bf2023-11-23T15:39:29ZengMDPI AGVaccines2076-393X2022-01-0110110310.3390/vaccines10010103Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter DataAndrew T. Lian0Jingcheng Du1Lu Tang2The Kinkaid School, Houston, TX 77024, USASchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USADepartment of Communication, Texas A&M University, College Station, TX 77843, USASocial media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems.https://www.mdpi.com/2076-393X/10/1/103COVID-19 vaccinesvaccine adverse eventsTwittermachine learningnatural language processing |
| spellingShingle | Andrew T. Lian Jingcheng Du Lu Tang Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data Vaccines COVID-19 vaccines vaccine adverse events machine learning natural language processing |
| title | Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data |
| title_full | Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data |
| title_fullStr | Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data |
| title_full_unstemmed | Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data |
| title_short | Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data |
| title_sort | using a machine learning approach to monitor covid 19 vaccine adverse events vae from twitter data |
| topic | COVID-19 vaccines vaccine adverse events machine learning natural language processing |
| url | https://www.mdpi.com/2076-393X/10/1/103 |
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