Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia

Social media platform like Twitter paved the way for easy information dissemination over the Internet. However, use of social media platform carries high probability of misinformation. In 2018, many parents decided to completely stop getting their children vaccinated due to the Dengvaxia scandal, wh...

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Main Author: ABRIGO, ANGELU BIANCA
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Published: Archīum Ateneo 2019
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Online Access:https://archium.ateneo.edu/theses-dissertations/4
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=2026402949&currentIndex=0&view=fullDetailsDetailsTab
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.theses-dissertations-10032021-09-13T05:56:00Z Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia ABRIGO, ANGELU BIANCA Social media platform like Twitter paved the way for easy information dissemination over the Internet. However, use of social media platform carries high probability of misinformation. In 2018, many parents decided to completely stop getting their children vaccinated due to the Dengvaxia scandal, which resulted to several measles outbreaks. Social media platform contributed to the fast information dissemination regarding the danger of Dengvaxia which created a negative perception towards vaccines in general. The study identified how information regarding the adverse effects of Dengvaxia spread on Twitter. Doc2vec was compared to n-gram neural network classification in order to identify Public Perception on Health Tweets (PPHT). The diffusion characteristics and its corresponding centrality measures was used to model the spread of PPHT and Non-PPHT. The result shows that bigram neural network has the highest performance measure with 85.57% accuracy, 85% precision, 86% recall and 85% F1 score. Moreover, the most influential PPHT comes from Youtube video shares, news agencies and its associates. While influential mediators are users that mostly post tweets to support a particular administration (i.e., Duterte admin). PPHT spreads deeper and has more replies than Non-PPHT, but has a lower structural virality and number of favorites. 2019-01-01T08:00:00Z text https://archium.ateneo.edu/theses-dissertations/4 http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=2026402949&currentIndex=0&view=fullDetailsDetailsTab Theses and Dissertations (All) Archīum Ateneo Twitter Medical informatics Public health -- Information services Social media in medicine Health -- Computer network resources Vaccination -- Philippines -- Case studies.Data mining. Health Information Technology Medical Education Public Health Education and Promotion
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Twitter
Medical informatics
Public health -- Information services
Social media in medicine
Health -- Computer network resources
Vaccination -- Philippines -- Case studies.Data mining.
Health Information Technology
Medical Education
Public Health Education and Promotion
spellingShingle Twitter
Medical informatics
Public health -- Information services
Social media in medicine
Health -- Computer network resources
Vaccination -- Philippines -- Case studies.Data mining.
Health Information Technology
Medical Education
Public Health Education and Promotion
ABRIGO, ANGELU BIANCA
Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
description Social media platform like Twitter paved the way for easy information dissemination over the Internet. However, use of social media platform carries high probability of misinformation. In 2018, many parents decided to completely stop getting their children vaccinated due to the Dengvaxia scandal, which resulted to several measles outbreaks. Social media platform contributed to the fast information dissemination regarding the danger of Dengvaxia which created a negative perception towards vaccines in general. The study identified how information regarding the adverse effects of Dengvaxia spread on Twitter. Doc2vec was compared to n-gram neural network classification in order to identify Public Perception on Health Tweets (PPHT). The diffusion characteristics and its corresponding centrality measures was used to model the spread of PPHT and Non-PPHT. The result shows that bigram neural network has the highest performance measure with 85.57% accuracy, 85% precision, 86% recall and 85% F1 score. Moreover, the most influential PPHT comes from Youtube video shares, news agencies and its associates. While influential mediators are users that mostly post tweets to support a particular administration (i.e., Duterte admin). PPHT spreads deeper and has more replies than Non-PPHT, but has a lower structural virality and number of favorites.
format text
author ABRIGO, ANGELU BIANCA
author_facet ABRIGO, ANGELU BIANCA
author_sort ABRIGO, ANGELU BIANCA
title Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
title_short Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
title_full Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
title_fullStr Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
title_full_unstemmed Modeling the spread of health information using social network analysis : understanding public perception on Dengvaxia
title_sort modeling the spread of health information using social network analysis : understanding public perception on dengvaxia
publisher Archīum Ateneo
publishDate 2019
url https://archium.ateneo.edu/theses-dissertations/4
http://rizalls.lib.admu.edu.ph/#section=resource&resourceid=2026402949&currentIndex=0&view=fullDetailsDetailsTab
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