Abstract
Significant growth in health information sharing through Twitter is making it a compelling source for health-related information. Recent health research studies show Twitter data has been used for disease surveillance, health promotion, sentiment analysis, and perhaps has potential for clinical decision support. However, identifying health-related tweets in these massive Twitter datasets is challenging. With the increasing global prevalence of diabetes, user-generated health content in Twitter can be useful. Therefore, this preliminary study aims to classify diabetes-related tweets into meaningful health-related categories. Using an ensemble of neural network and stochastic gradient descent classifiers, we classified 13,667 diabetes-related tweets into five clusters. About 25.7% of the tweets were clustered as health-related, where 9.3% were classified as Treatment & Medication, 9.9% as Preventive Measures and 6.5% as Symptoms & Causes. More than 70% were clustered as Others. Analysing hashtags of tweets clustered in each of the categories showed significant relevance to health-related information.
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Osop, H., Hasan, R., Lee, C.S., Neo, C.Y., Foo, C.K., Saurabh, A. (2020). Diabetweets: Analysis of Tweets for Health-Related Information. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-60703-6_65
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DOI: https://doi.org/10.1007/978-3-030-60703-6_65
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