The Digital Distribution of Public Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal Infodemiology Study

Mahoney, L. Meghan; Tang, Tang; Jiao, Kai; Ulrich‐Schad, Jessica D.

doi:10.2196/publichealth.3310

Cited by 34 publications

(26 citation statements)

References 22 publications

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“…Despite some research demonstrating that Web-based vaccine information can have a largely negative sentiment [11], our findings show that a great percentage of tweets about HPV vaccine had a positive sentiment, helping to validate findings on the same topic [24-27]. Furthermore, over one-quarter of these positive HPV vaccine tweets mentioned prevention or protection.…”

Section: Discussionsupporting

confidence: 38%

“…Researchers are just beginning to assess the extent and type of discourse about the HPV vaccine on Twitter, although methods are varied [24-27]. This emerging area of communication research provides an opportunity for interdisciplinary teams among the fields of public health, health communication, and data science to strengthen the science and methodology in this growing area of research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter

Massey¹,

Leader²,

Yom-Tov³

et al. 2016

J Med Internet Res

127

100

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BackgroundHuman papillomavirus (HPV) is the most common sexually transmitted infection in the United States. There are several vaccines that protect against strains of HPV most associated with cervical and other cancers. Thus, HPV vaccination has become an important component of adolescent preventive health care. As media evolves, more information about HPV vaccination is shifting to social media platforms such as Twitter. Health information consumed on social media may be especially influential for segments of society such as younger populations, as well as ethnic and racial minorities.ObjectiveThe objectives of our study were to quantify HPV vaccine communication on Twitter, and to develop a novel methodology to improve the collection and analysis of Twitter data.MethodsWe collected Twitter data using 10 keywords related to HPV vaccination from August 1, 2014 to July 31, 2015. Prospective data collection used the Twitter Search API and retrospective data collection used Twitter Firehose. Using a codebook to characterize tweet sentiment and content, we coded a subsample of tweets by hand to develop classification models to code the entire sample using machine learning procedures. We also documented the words in the 140-character tweet text most associated with each keyword. We used chi-square tests, analysis of variance, and nonparametric equality of medians to test for significant differences in tweet characteristic by sentiment.ResultsA total of 193,379 English-language tweets were collected, classified, and analyzed. Associated words varied with each keyword, with more positive and preventive words associated with “HPV vaccine” and more negative words associated with name-brand vaccines. Positive sentiment was the largest type of sentiment in the sample, with 75,393 positive tweets (38.99% of the sample), followed by negative sentiment with 48,940 tweets (25.31% of the sample). Positive and neutral tweets constituted the largest percentage of tweets mentioning prevention or protection (20,425/75,393, 27.09% and 6477/25,110, 25.79%, respectively), compared with only 11.5% of negative tweets (5647/48,940; P<.001). Nearly one-half (22,726/48,940, 46.44%) of negative tweets mentioned side effects, compared with only 17.14% (12,921/75,393) of positive tweets and 15.08% of neutral tweets (3787/25,110; P<.001).ConclusionsExamining social media to detect health trends, as well as to communicate important health information, is a growing area of research in public health. Understanding the content and implications of conversations that form around HPV vaccination on social media can aid health organizations and health-focused Twitter users in creating a meaningful exchange of ideas and in having a significant impact on vaccine uptake. This area of research is inherently interdisciplinary, and this study supports this movement by applying public health, health communication, and data science approaches to extend methodologies across fields.

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Section: Discussionsupporting

confidence: 38%

Section: Introductionmentioning

confidence: 99%

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter

Massey¹,

Leader²,

Yom-Tov³

et al. 2016

J Med Internet Res

127

100

View full text Add to dashboard Cite

show abstract

“…Two studies have independently examined responses on Twitter to specific controversial events including US Representative Michele Bachmann’s claim that HPV vaccines could cause ‘mental retardation’ and Katie Couric’s television segment ‘HPV Vaccine Controversy’ that aired on 4 December 2013. 53 75 Mahoney et al evaluated 200 social media posts before and after Bachmann’s comments on the Today Show and found that though most media was positive in tone, compared with Google News, Twitter disseminated more positive HPV vaccine articles and also used more personal accounts as a reference source. 53 In contrast, using a random sample of 3595 tweets, Bahk et al found that most sentiment on Twitter towards HPV vaccines before Katie Couric’s episode was negative, and while there was a decrease of negative sentiment immediately after the show aired, negative sentiment returned to baseline after 2 weeks.…”

Section: Discussionmentioning

confidence: 99%

Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK

et al. 2017

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ObjectiveOpposition to human papillomavirus (HPV) vaccination is common on social media and has the potential to impact vaccine coverage. This study aims to conduct an international comparison of the proportions of tweets about HPV vaccines that express concerns, the types of concerns expressed and the social connections among users posting about HPV vaccines in Australia, Canada and the UK.DesignUsing a cross-sectional design, an international comparison of English language tweets about HPV vaccines and social connections among Twitter users posting about HPV vaccines between January 2014 and April 2016 was conducted. The Health Belief Model, one of the most widely used theories in health psychology, was used as the basis for coding the types of HPV vaccine concerns expressed on Twitter.SettingThe content of tweets and the social connections between users who posted tweets about HPV vaccines from Australia, Canada and the UK.Population16 789 Twitter users who posted 43 852 tweets about HPV vaccines.Main outcome measuresThe proportions of tweets expressing concern, the type of concern expressed and the proportions of local and international social connections between users.ResultsTweets expressing concerns about HPV vaccines made up 14.9% of tweets in Canada, 19.4% in Australia and 22.6% in the UK. The types of concerns expressed were similar across the three countries, with concerns related to ‘perceived barriers’ being the most common. Users expressing concerns about HPV vaccines in each of the three countries had a relatively high proportion of international followers also expressing concerns.ConclusionsThe proportions and types of HPV vaccine concerns expressed on Twitter were similar across the three countries. Twitter users who mostly expressed concerns about HPV vaccines were better connected to international users who shared their concerns compared with users who did not express concerns about HPV vaccines.

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“…Examples that are relevant to our research include the use of topic modeling to extract tobacco-related tweets in the United States [ 18 ] and the surveillance of information about the misunderstanding and misuse of antibiotics from online media [ 19 ]. There is a growing area of research considering the spread of information, news, and opinions about vaccines [ 20 - 24 ], and research in this area focuses on measuring associations between misinformation, beliefs, and decision making across a range of community health practices [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection

Surian¹,

Nguyen²,

Kennedy³

et al. 2016

J Med Internet Res

170

127

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BackgroundIn public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes.ObjectiveOur aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter.MethodsThe study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines.ResultsWe analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy.ConclusionsThe use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines.

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The Digital Distribution of Public Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal Infodemiology Study

Cited by 34 publications

References 22 publications

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter

Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter

Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK

Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection

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