Understanding the dynamics of health communication on twitter using epidemiological modeling

• Main Author: Yusoph, Feeroz Razul
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Communication in medicine; Social media; Communication Technology and New Media; Social Media
• Online Access: https://animorepository.dlsu.edu.ph/etdm_math/3; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1002&context=etdm_math
• Institution: De La Salle University

Social media has become a critical component in spreading public health awareness to help the public media be informed about their health and influence them to exercise healthy lifestyles. It is important to study health communication as it influences public health protection that causes positive behavior changes in individuals and as a result, help reduce the spread of pandemics by understanding how information spread among the general public. Social networks like Twitter is a widely used communication environment used by individuals, businesses, and healthcare organizations to share and broadcast information in the form of tweets. Epidemiological models are used to understand how information spreads on Twitter where it divides individuals (or users) into groups and simulates their interaction with each other. In this study, the SEIR model is adapted to model how health communication is disseminated over Twitter. Two models are presented: a basic twitter interaction model and a model wherein the sentiments of tweets are considered. To our knowledge, these models are a first of its kind to study health communication dynamics on Twitter and to understand the behavior of users based on the sentiments of tweets. In the basic interaction model (TwitHComm), we compared the dynamics of health communication spreading of @WHO and @DOHgovph and have found that the tweet data obtained from @DOHgovph does not achieve an epidemic state where @WHO does. In the model where sentiments were considered (TwitHCommS), despite increasing the number of positive sentiment tweets in the simulation, users on Twitter are influenced by negative sentiments and this is caused by the fact that there is a higher rate negative sentiments among the users. Building relationships among users on Twitter is crucial for health organizations in order to develop trust and engage users on Twitter for positive sentiment tweets to persist.