VaxInsight: an artificial intelligence system to access large-scale public perceptions of vaccination from social media

Vaccination is considered one of the greatest public health achievements of the 20th century. A high vaccination rate is required to reduce the prevalence and incidence of vaccine-preventable diseases. However, in the last two decades, there has been a significant and increasing number of people who refuse or delay getting vaccinated and who prohibit their children from receiving vaccinations. Importantly, under-vaccination is associated with infectious disease outbreaks. A good understanding of public perceptions regarding vaccinations is important if we are to develop effective vaccination promotion strategies. Traditional methods of research, such as surveys, suffer limitations that impede our understanding of public perceptions, including resources cost, delays in data collection and analysis, especially in large samples. The popularity of social media (e.g. Twitter), combined with advances in artificial intelligence algorithms (e.g. natural language processing, deep learning), open up new avenues for accessing large scale data on public perceptions related to vaccinations. This dissertation reports on an original and systematic effort to develop artificial intelligence algorithms that will increase our ability to use Twitter discussions to understand vaccine-related perceptions and intentions. The research is framed within the perspectives offered by grounded behavior change theories. Tweets concerning the human papillomavirus (HPV) vaccine were used to accomplish three major aims: 1) Develop a deep learning-based system to better understand public perceptions of the HPV vaccine, using Twitter data and behavior change theories; 2) Develop a deep learning-based system to infer Twitter users’ demographic characteristics (e.g. gender and home location) and investigate demographic differences in public perceptions of the HPV vaccine; 3) Develop a web-based interactive visualization system to monitor real-time Twitter discussions of the HPV vaccine. For Aim 1, the bi-directional long short-term memory (LSTM) network with attention mechanism outperformed traditional machine learning and competitive deep learning algorithms in mapping Twitter discussions to the theoretical constructs of behavior change theories. Domain-specific embedding trained on HPV vaccine-related Twitter corpus by fastText algorithms further improved performance on some tasks. Time series analyses revealed evolving trends of public perceptions regarding the HPV vaccine. For Aim 2, the character-based convolutional neural network model achieved favorable state-of-the-art performance in Twitter gender inference on a Public Author Profiling challenge. The trained models then were applied to the Twitter corpus and they identified gender differences in public perceptions of the HPV vaccine. The findings on gender differences were largely consistent with previous survey-based studies. For the Twitter users’ home location inference, geo-tagging was framed as text classification tasks that resulted in a character-based recurrent neural network model. The model outperformed machine learning and deep learning baselines on home location tagging. Interstate variations in public perceptions of the HPV vaccine also were identified. For Aim 3, a prototype web-based interactive dashboard, VaxInsight, was built to synthesize HPV vaccine-related Twitter discussions in a comprehendible format. The usability test of VaxInsight showed high usability of the system. Notably, this maybe the first study to use deep learning algorithms to understand Twitter discussions of the HPV vaccine within the perspective of grounded behavior change theories. VaxInsight is also the first system that allows users to explore public health beliefs of vaccine related topics from Twitter. Thus, the present research makes original and systematical contributions to medical informatics by combining cutting-edge artificial intelligence algorithms and grounded behavior change theories. This work also builds a foundation for the next generation of real-time public health surveillance and research

Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

Real-time access to information during a pandemic is crucial for mobilizing a response. A sentiment analysis of Twitter posts from the first 90 days of the COVID-19 pandemic was conducted. In particular, 2 million English tweets were collected from users in the United States that contained the word ‘covid’ between January 1, 2020 and March 31, 2020. Sentiments were used to model the new case and death counts using data from this time. The results of linear regression and k-nearest neighbors indicate that public sentiments on social media accurately predict both same-day and near future counts of both COVID-19 cases and deaths. Public health officials can use this knowledge to assist in responding to adverse public health events. Additionally, implications for future research and theorizing of social media’s impact on health behaviors are discussed

Detecting adherence to the recommended childhood vaccination schedule from user-generated content in a US parenting forum

Vaccine hesitancy is considered as one of the leading causes for the resurgence of vaccine preventable diseases. A non-negligible minority of parents does not fully adhere to the recommended vaccination schedule, leading their children to be partially immunized and at higher risk of contracting vaccine preventable diseases. Here, we leverage more than one million comments of 201,986 users posted from March 2008 to April 2019 on the public online forum BabyCenter US to learn more about such parents. For 32% with geographic location, we find the number of mapped users for each US state resembling the census population distribution with good agreement. We employ Natural Language Processing to identify 6884 and 10,131 users expressing their intention of following the recommended and alternative vaccination schedule, respectively RSUs and ASUs. From the analysis of their activity on the forum we find that ASUs have distinctly different interests and previous experiences with vaccination than RSUs. In particular, ASUs are more likely to follow groups focused on alternative medicine, are two times more likely to have experienced adverse events following immunization, and to mention more serious adverse reactions such as seizure or developmental regression. Content analysis of comments shows that the resources most frequently shared by both groups point to governmental domains (.gov). Finally, network analysis shows that RSUs and ASUs communicate between each other (indicating the absence of echo chambers), however with the latter group being more endogamic and favoring interactions with other ASUs. While our findings are limited to the specific platform analyzed, our approach may provide additional insights for the development of campaigns targeting parents on digital platforms.Postprint (published version

Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining

The aim of this study was to compare impressions of COVID-19 vaccination and influenza vaccination in Japan by analyzing social media (Twitter®) using a text-mining method. We obtained 10,000 tweets using the keywords “corona vaccine” and “influenza vaccine” on 15 December 2022 and 19 February 2023. We then counted the number of times the words were used and listed frequency of these words by a text-mining method called KH Coder. We also investigated concepts in the data using groups of words that often appeared together or groups of documents that contained the same words using multi-dimensional scaling (MDS). “Death” in relation to corona vaccine and “severe disease” for influenza vaccine were frequently used on 15 December 2022. The number of times the word “death” was used decreased, “after effect” was newly recognized for corona vaccine, and “severe disease” was not used in relation to influenza vaccine. Through this comprehensive analysis of social media data, we observed distinct variations in public perceptions of corona vaccination and influenza vaccination in Japan. These findings provide valuable insights for public health authorities and policymakers to better understand public sentiment and tailor their communication strategies accordingly

An Analysis of COVID-19 Vaccine Hesitancy in the U.S. at the County Level

Reluctance or refusal to get vaccinated, referred to as vaccine hesitancy (VH), has hindered the efforts of COVID-19 vaccination campaigns. It is important to understand what factors impact VH behavior. This information can help design public health interventions that could potentially increase vaccine uptake. We develop a random forest (RF) classification model that uses a wide variety of data to determine what factors affected VH at the county level during 2021. We consider static factors (such as gender, race, political affiliation, etc.) and dynamic factors (such as Google searches, social media postings, Stringency Index, etc.). Our model found political affiliation and the number of Google searches to be the most relevant factors in determining VH behavior. The RF classification model grouped counties of the U.S. into 5 clusters. VH is lowest in cluster 1 and highest in cluster 5. Most of the people who live in cluster 1 are democrat, are more internet-inquisitive (are more prone to seek information from multiple sources on the internet), have the longest life expectancy, have a college degree, have the highest income per capita, live in metropolitan areas. Most people who live in cluster 5 are republicans, are the least internet-inquisitive, have the shortest life expectancy, do not have a college degree, have the lowest income per capita, and live in non-metropolitan areas. Our model found that counties in cluster 1 were most responsive to vaccination-related policies and COVID-19 restrictions. These strategies did not have an impact on the VH of counties in cluster 5.Comment: 28 pages, 12 figures, 4 table

The American Academy of Health Behavior 2024 Annual Scientific Meeting: Health Communication, (Mis-)Information, and Behavior: Leveraging Technology for Behavioral Interventions and Health Behavior Research

The American Academy of Health Behavior (AAHB) hosted its 24th Annual Scientific Meeting at The DeSoto Hotel in Savannah, Georgia on April 14-17, 2024. The meeting\u27s theme was “Health Communication, (Mis-)Information, and Behavior: Leveraging Technology for Behavioral Interventions and Health Behavior Research . This publication describes the meeting theme and includes the refereed abstracts presented at the 2024 Annual Scientific Meeting

Post licensure surveillance of Human Papillomavirus vaccine, vaccine adverse event reporting system, 2010-2017

Currently, there are three approved and commercially available Human Papillomavirus (HPV) vaccines. These are Gardasil®, a quadrivalent HPV, Cervarix®, a bivalent vaccine, and Gardasil®9, a nonvalent HPV and they were first approved in 2006, 2009, and 2014, respectively for use in females. Approvals for use in males followed a few years later for Gardasil® and Gardasil®9. The acceptance of HPV vaccination has been a challenge, including cost, cultural views, parent’s acceptance, safety, and adverse events of the vaccine. Vaccination acceptance is mainly influenced by safety reports and, unfortunately, also by misinformation from social media. The United States (US) Food and Drug Administration (FDA) collects and maintains post-marketing products’ safety data, including vaccines. These safety data or adverse event data are collected through several methods, including chart reviews and reporting systems. The use of data mining has shown to be useful in extracting critical information from a large dataset. The adverse event datasets associated with commercially available HPV vaccines for years 2010-2017 from the Vaccine Adverse Event Reporting System (VAERS) were analyzed using SAS Text Analytics. The results showed that most of the detected adverse events were commonly reported, and many are non-serious associated with teenage and young adult patients. In the 7-year data analyzed, authors found that most adverse events terms were associated with nervous system disorders (n=1251) followed with general disorders and administration site conditions (n=1192). In addition, death, and serious terms (Guillain-Barre syndrome, seizure, anaphylactic shock) were also identified. In conclusion, this study did not detect safety signals associated with HPV vaccines between 2010 to 2017. Big data analysis will serve as a baseline for analysis of this ongoing surveillance in the pharmacovigilance field in the future