ArticleHuman vaccines & immunotherapeutics2019
A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017).
Article in Human vaccines & immunotherapeutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.Computers in biology and medicine · 2021Pooled it
- Characteristics of Antivaccine Messages on Social Media: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Study of Human Papillomavirus Vaccine-Related Social Media Content Across Eight Social Media Platforms: Scoping Review of Communication Content, Information Sources, and Analytical Approaches.Journal of medical Internet research · 2026Article
- Human Papillomavirus Vaccine Discourse and Sentiment on Reddit Before and After COVID-19: Mixed Methods Retrospective Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Review
- Efficient information extraction using LLMs and knowledge distillation: A study on HPV health communication.PLOS digital health · 2026Article
- Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Article
- Public perception of the resumption of HPV vaccine recommendation in Japan: Twitter content analysis.Health promotion international · 2023Article
- Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.Journal of medical Internet research · 2023Article
- HPV vaccine narratives on Twitter during the COVID-19 pandemic: a social network, thematic, and sentiment analysis.BMC public health · 2023Article
- Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse.Journal of behavioral medicine · 2023Article
- Digital tools, multidisciplinarity and innovation for communicating vaccine safety in the COVID-19 era.Human vaccines & immunotherapeutics · 2022Article
- Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media.Scientific reports · 2021Article
- Trends in Human Papillomavirus Vaccine Safety Concerns and Adverse Event Reporting in the United States.JAMA network open · 2021Article
- Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.Journal of medical Internet research · 2021Article
- Understanding Human Papillomavirus Vaccine Promotions and Hesitancy in Northern California Through Examining Public Facebook Pages and Groups.Frontiers in digital health · 2021Article
- Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers.Journal of medical Internet research · 2020Article
- Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy.Human vaccines & immunotherapeutics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, we developed a natural language processing (NLP) framework to investigate the opinions on HPV vaccination reflected on Twitter over a 10-year period - 2008-2017. The NLP framework includes sentiment analysis, entity analysis, and artificial intelligence (AI)-based phrase association mining. The sentiment analysis demonstrates the sentiment fluctuation over the past 10 years. The results show that there are more negative tweets in 2008 to 2011 and 2015 to 2016. The entity extraction and analysis help to identify the organization, geographical location and events entities associated with the negative and positive tweets. The results show that the organization entities such as FDA, CDC and Merck occur in both negative and positive tweets of almost every year, whereas the geographical location entities mentioned in both negative and positive tweets change from year to year. The reason is because of the specific events that happened in those different locations. The objective of the AI-based phrase association mining is to identify the main topics reflected in both negative and positive tweets and detailed tweet content. Through the phrase association mining, we found that the main negative topics on Twitter include "injuries", "deaths", "scandal", "safety concerns", and "adverse/side effects", whereas the main positive topics include "cervical cancers", "cervical screens", "prevents", and "vaccination campaigns". We believe the results of this research can help public health researchers better understand the nature of social media influence on HPV vaccination attitudes and to develop strategies to counter the proliferation of misinformation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.