Big social data analytics for public health: Facebook engagement and performance

TY - GEN

T1 - Big social data analytics for public health

T2 - 18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016

AU - Straton, Nadiya

AU - Hansen, Kjeld

AU - Mukkamala, Raghava Rao

AU - Hussain, Abid

AU - Grønli, Tor Morten

AU - Langberg, Henning

AU - Vatrapu, Ravi

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1109/HealthCom.2016.7749497

DO - 10.1109/HealthCom.2016.7749497

M3 - Article in proceedings

AN - SCOPUS:85006380060

BT - 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016

PB - IEEE

Y2 - 14 September 2016 through 17 September 2016

ER -