Prediction of vaccine hesitancy based on social media traffic among Israeli parents using machine learning strategies

Abstract Introduction Vaccines have contributed to substantial reductions of morbidity and mortality from vaccine-preventable diseases, mainly in children. However, vaccine hesitancy was listed by the World Health Organization (WHO) in 2019 as one of the top ten threats to world health. Aim To emplo...

Full description

Saved in:
Bibliographic Details
Main Authors: Shirly Bar-Lev (Author), Shahar Reichman (Author), Zohar Barnett-Itzhaki (Author)
Format: Book
Published: BMC, 2021-08-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_f9c95e36c26c44429e50b1ca8be7b7b0
042 |a dc 
100 1 0 |a Shirly Bar-Lev  |e author 
700 1 0 |a Shahar Reichman  |e author 
700 1 0 |a Zohar Barnett-Itzhaki  |e author 
245 0 0 |a Prediction of vaccine hesitancy based on social media traffic among Israeli parents using machine learning strategies 
260 |b BMC,   |c 2021-08-01T00:00:00Z. 
500 |a 10.1186/s13584-021-00486-6 
500 |a 2045-4015 
520 |a Abstract Introduction Vaccines have contributed to substantial reductions of morbidity and mortality from vaccine-preventable diseases, mainly in children. However, vaccine hesitancy was listed by the World Health Organization (WHO) in 2019 as one of the top ten threats to world health. Aim To employ machine-learning strategies to assess how on-line content regarding vaccination affects vaccine hesitancy. Methods We collected social media posts and responses from vaccination discussion groups and forums on leading social platforms, including Facebook and Tapuz (A user content website that contains blogs and forums). We investigated 65,603 records of children aged 0-6 years who are insured in Maccabi's Health Maintenance Organization (HMO). We applied three machine learning algorithms (Logistic regression, Random forest and Neural networks) to predict vaccination among Israeli children, based on demographic and social media traffic. Results Higher hesitancy was associated with more social media traffic, for most of the vaccinations. The addition of the social media traffic features improved the performances of most of the models. However, for Rota virus, Hepatitis A and hepatitis B, the performances of all algorithms (with and without the social media features) were close to random (accuracy up to 0.63 and F1 up to 0.65). We found a negative association between on-line discussions and vaccination. Conclusions There is an association between social media traffic and vaccine hesitancy. Policy makers are encouraged to perceive social media as a main channel of communication during health crises. Health officials and experts are encouraged to take part in social media discussions, and be equipped to readily provide the information, support and advice that the public is looking for, in order to optimize vaccination actions and to improve public health 
546 |a EN 
690 |a Childhood vaccination 
690 |a Epidemiology 
690 |a Social media 
690 |a Machine learning 
690 |a Public health 
690 |a Medicine (General) 
690 |a R5-920 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Israel Journal of Health Policy Research, Vol 10, Iss 1, Pp 1-8 (2021) 
787 0 |n https://doi.org/10.1186/s13584-021-00486-6 
787 0 |n https://doaj.org/toc/2045-4015 
856 4 1 |u https://doaj.org/article/f9c95e36c26c44429e50b1ca8be7b7b0  |z Connect to this object online.