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Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
12 X users

Citations

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87 Dimensions

Readers on

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179 Mendeley
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Title
Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0469-6
Pubmed ID
Authors

Jingcheng Du, Jun Xu, Hsing-Yi Song, Cui Tao

Abstract

As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved human papillomavirus (HPV) vaccines. Understanding public opinion towards HPV vaccines, especially concerns on social media, is of significant importance for HPV vaccination promotion. In this study, we leveraged a hierarchical machine learning based sentiment analysis system to extract public opinions towards HPV vaccines from Twitter. English tweets containing HPV vaccines-related keywords were collected from November 2, 2015 to March 28, 2016. Manual annotation was done to evaluate the performance of the system on the unannotated tweets corpus. Followed time series analysis was applied to this corpus to track the trends of machine-deduced sentiments and their associations with different days of the week. The evaluation of the unannotated tweets corpus showed that the micro-averaging F scores have reached 0.786. The learning system deduced the sentiment labels for 184,214 tweets in the collected unannotated tweets corpus. Time series analysis identified a coincidence between mainstream outcome and Twitter contents. A weak trend was found for "Negative" tweets that decreased firstly and began to increase later; an opposite trend was identified for "Positive" tweets. Tweets that contain the worries on efficacy for HPV vaccines showed a relative significant decreasing trend. Strong associations were found between some sentiments ("Positive", "Negative", "Negative-Safety" and "Negative-Others") with different days of the week. Our efforts on sentiment analysis for newly approved HPV vaccines provide us an automatic and instant way to extract public opinion and understand the concerns on Twitter. Our approaches can provide a feedback to public health professionals to monitor online public response, examine the effectiveness of their HPV vaccination promotion strategies and adjust their promotion plans.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 179 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 33 18%
Student > Ph. D. Student 27 15%
Researcher 16 9%
Student > Bachelor 13 7%
Student > Doctoral Student 8 4%
Other 34 19%
Unknown 48 27%
Readers by discipline Count As %
Computer Science 35 20%
Medicine and Dentistry 24 13%
Social Sciences 11 6%
Engineering 10 6%
Nursing and Health Professions 9 5%
Other 30 17%
Unknown 60 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 15 April 2018.
All research outputs
#5,775,787
of 22,986,950 outputs
Outputs from BMC Medical Informatics and Decision Making
#512
of 2,003 outputs
Outputs of similar age
#90,756
of 313,319 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#11
of 41 outputs
Altmetric has tracked 22,986,950 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,003 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 74% 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 313,319 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 41 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 70% of its contemporaries.