Dataset Information

Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets.

ABSTRACT: Text analysis has been used by scholars to research attitudes toward vaccination and is particularly timely due to the rise of medical misinformation via social media. This study uses a sample of 9581 vaccine-related tweets in the period January 1, 2019 to April 5, 2019. The time period is of the essence because during this time, a measles outbreak was prevalent throughout the United States and a public debate was raging. Sentiment analysis is applied to the sample, clustering the data into topics using the term frequency-inverse document frequency (TF-IDF) technique. The analyses suggest that most (about 77%) of the tweets focused on the search for new/better vaccines for diseases such as the Ebola virus, human papillomavirus (HPV), and the flu. Of the remainder, about half concerned the recent measles outbreak in the United States, and about half were part of ongoing debates between supporters and opponents of vaccination against measles in particular. While these numbers currently suggest a relatively small role for vaccine misinformation, the concept of herd immunity puts that role in context. Nevertheless, going forward, health experts should consider the potential for the increasing spread of falsehoods that may get firmly entrenched in the public mind.

SUBMITTER: Raghupathi V

PROVIDER: S-EPMC7277574 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets.

Raghupathi Viju V Ren Jie J Raghupathi Wullianallur W

International journal of environmental research and public health 20200515 10

Text analysis has been used by scholars to research attitudes toward vaccination and is particularly timely due to the rise of medical misinformation via social media. This study uses a sample of 9581 vaccine-related tweets in the period January 1, 2019 to April 5, 2019. The time period is of the essence because during this time, a measles outbreak was prevalent throughout the United States and a public debate was raging. Sentiment analysis is applied to the sample, clustering the data into topi ...[more]

PMID: 32429223

Similar Datasets

Project description:BACKGROUND:Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response. OBJECTIVE:The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set. METHODS:We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word "measles" posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings. RESULTS:Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642). CONCLUSIONS:The proposed scheme can successfully classify the public's opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.

Dataset Information

Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets.

Publications

Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets