HPV vaccine coverage in Australia and associations with HPV vaccine information exposure among Australian Twitter users

Abstract

Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia. Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors. Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson's R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [−0.02 to 0.68]; male: 0.36 [−0.04 to 0.66]). In Australia, positivelyframed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular. Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

“…Chan et al [74], who studied influenza vaccination in the United States, used LDA to create 10 topics in which some shared similar attributes with the themes of Jamison et al [85], such as vaccine science, safety concerns, and conspiracy theories. Some, but not all, of the similar themes, focus areas, and topics could also be seen in the analyses of tweets about vaccination regardless of virus types such as those in the studies surrounding HPV vaccinations [73,[87][88][89][90].…”

“…In addition, a sound reason or calculation is needed to support the preset number of topics, which would affect the results. Some previous studies generated a rather high number of topics (30-100) using an LDA or DMM model, and then manually grouped the topics into themes [73,85,89]. However, there was risk of bias since the content of each topic was not reported in detail, and the contents of the themes could be mixed, which is difficult to interpret.…”

Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

“…The “Gensim” package in Python was used to implement the LDA model. Through more than 10 tests and some reference studies [ 16 , 24 , 25 ], we set 10 themes every day, and each theme contains 15 words.…”

The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation

“…One possible explanation for why increased government responsiveness can facilitate the spread of positive sentiment is that the government, as a network leader, influences the public through high communication activity, credibility, network centrality, and the use of affective, assertive, and linguistic diversity in their online messages [ 36 ], and uses mass media to amplify public sentiment [ 37 ]. A study showed that exposure to HPV (Human Papilloma Virus) information was associated with the degree of HPV vaccination [ 38 ]. This suggests that the government communicates risk to the public through the repetition of information and emphasizes the good attitude of the government in dealing with negative events [ 39 ], thus the government has gained the trust of the people.…”