Emotions towards the COVID-19 vaccine, whether positive or negative, preview subsequent vaccination rates and find relevant Twitter-posted studies. The results provide new insights into the impact of social media on public health measures.
The study, conducted by researchers at the Courtant Institute of Mathematical Sciences and the NYU Grossman School of Medicine at New York University, showed that the positive sentiment towards vaccination, expressed on Twitter, continued a week later. Will increase The decline in vaccination coverage in the same area was followed by negative sentiment in the same area. Will decrease At the vaccination rate the next week.
In this study, we deployed a real-time big data analysis framework using sentiment analysis and natural language processing (NLP) algorithms. The system captures real-time tweets, identifies vaccine-related tweets, categorizes them by specific themes, provides sentiment analysis, and catalogs tweets as positive, negative, or neutral.
“We need to understand the hesitation of vaccines and the impact of social media on vaccine production and dissemination,” said Megan Coffee, MD, PhD and clinical assistant professor of the Department of Infectious Diseases and Immunology, School of Medicine, NYU Grossman School of Medicine. increase.One of the authors of a paper published in a journal Clinical infections.. “This is the first step in creating a barometer for tracking emotions and themes associated with vaccine hesitation.”
To track and understand the impact of social media on vaccine hesitation on this epidemic and future epidemics, as the COVID epidemic put many of us in front of computers and vaccine hesitation shaped the epidemic. You need a tool like this. “
Anas Bali, a clinical associate professor of computer science and author of the dissertation at the Institute of Mathematics and Science, New York University
Researchers say vaccination may help end the ongoing surge in COVID pandemics and new varieties. However, they observe that vaccine hesitation undermines the effects of vaccination individually and collectively. Further complicating this is the role of social media, which increasingly amplifies both immunization and false information, specifically how these platforms affect immunization rates. I am asking questions about.
To address this, the authors of the paper have developed big data analysis applications based on Natural Language Processing (NLP), Sentiment Analysis (SA), and Amazon Web Services (AWS).
This tool allowed researchers to track vaccine-related topics that appear in dozens of phrases. Topics include conspiracy, fear, freedom of health, natural choices, side effects, safety, trust / distrust, vaccine companies, established sources, hesitation, and more. Phrases related to these topics allowed vaccination to be given an “emotion score”-positive, negative, or neutral.
We also used the Institute of Electrical and Electronics Engineers (IEEE) data port dataset, which is a commonly deployed dataset. This dataset tagged the emotional scores of tweets related to coronavirus by geographic location in the United States. The dataset analyzed contained over 23,000 vaccine-related tweets from March 20, 2021 to July 20, 2021.
Overall, the data show that once all adults have access to the vaccine, positive sentiment will increase in certain parts of the United States around mid-April 2021 and vaccination rates will increase a week later. rice field. In contrast, in areas of reduced sentiment, vaccination rates continued to decline after a week.
In particular, the big data analysis framework is similar in positive and negative sentiment towards vaccines in the first months of the pandemic, before the start of vaccine deployment at the end of 2020. It showed that the positive emotions were slightly higher. in contrast, rear Vaccine deployment has begun, and negative emotional tweets have outnumbered positive ones.
“Because vaccination rates have been found to be tracked locally by Twitter’s vaccination sentiment, more advanced analytical tools can predict changes in vaccine intake and for targeted social media campaigns and vaccination strategies. It can lead to development, “said Bari, who heads the predictive analysis of the Courtant Institute. And AI Institute.
“This way we can start identifying patterns of vaccine repellent at any time and place,” Coffee adds. “But it can only monitor and influence the ever-changing hesitation of vaccines. More to build confidence in life-saving vaccines and counteract the negative effects of vaccines. Work is required. “
Bali, A. , et al. (2022) Investigation of hesitation of coronavirus disease 2019 vaccine on Twitter using sentiment analysis and natural language processing algorithms. Clinical infection. doi.org/10.1093/cid/ciac141..
https://industrialnews.co.uk/sentiment-toward-covid-19-vaccines-previews-subsequent-vaccination-rates-twitter-study-finds/?utm_source=rss&utm_medium=rss&utm_campaign=sentiment-toward-covid-19-vaccines-previews-subsequent-vaccination-rates-twitter-study-finds Emotions about the COVID-19 vaccine preview subsequent vaccination rates, Twitter studies find