Abstract
In recent years, social media has offered new opportunities for interaction and distribution of public health information within and across organisations. In this paper, we analysed data from Facebook walls of 153 public organisations using unsupervised machine learning techniques to understand the characteristics of user engagement and post performance. Our analysis indicates an increasing trend of user engagement on public health posts during recent years. Based on the clustering results, our analysis shows that Photo and Link type posts are most favourable for high and medium user engagement respectively.
| Original language | English |
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| Title of host publication | 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016 |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2016 |
| Article number | 7749497 |
| ISBN (Electronic) | 978-1-5090-3370-6 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016 - Munich, Germany Duration: 14 Sept 2016 → 17 Sept 2016 |
Conference
| Conference | 18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016 |
|---|---|
| Country/Territory | Germany |
| City | Munich |
| Period | 14/09/2016 → 17/09/2016 |
