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

Overview of attention for article published in Journal of Medical Internet Research, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

policy
1 policy source
twitter
22 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
112 Mendeley
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Title
Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection
Published in
Journal of Medical Internet Research, August 2016
DOI 10.2196/jmir.6045
Pubmed ID
Authors

Didi Surian, Dat Quoc Nguyen, Georgina Kennedy, Mark Johnson, Enrico Coiera, Adam G Dunn

Abstract

In 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. Our 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. The 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. We 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. The 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.

Twitter Demographics

The data shown below were collected from the profiles of 22 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Korea, Republic of 1 <1%
Unknown 110 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 26%
Student > Master 18 16%
Researcher 15 13%
Student > Doctoral Student 11 10%
Unspecified 10 9%
Other 29 26%
Readers by discipline Count As %
Computer Science 32 29%
Medicine and Dentistry 17 15%
Social Sciences 15 13%
Unspecified 13 12%
Psychology 7 6%
Other 28 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2019.
All research outputs
#1,076,983
of 13,565,334 outputs
Outputs from Journal of Medical Internet Research
#1,140
of 3,119 outputs
Outputs of similar age
#30,231
of 261,624 outputs
Outputs of similar age from Journal of Medical Internet Research
#34
of 84 outputs
Altmetric has tracked 13,565,334 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,119 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.8. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 261,624 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.