Standard
Big social data analytics for public health : Facebook engagement and performance. / Straton, Nadiya; Hansen, Kjeld; Mukkamala, Raghava Rao; Hussain, Abid; Grønli, Tor Morten; Langberg, Henning; Vatrapu, Ravi.
2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016. IEEE, 2016. 7749497.
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Straton, N, Hansen, K, Mukkamala, RR, Hussain, A, Grønli, TM
, Langberg, H & Vatrapu, R 2016,
Big social data analytics for public health: Facebook engagement and performance. i
2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016., 7749497, IEEE, 18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016, Munich, Tyskland,
14/09/2016.
https://doi.org/10.1109/HealthCom.2016.7749497APA
Straton, N., Hansen, K., Mukkamala, R. R., Hussain, A., Grønli, T. M.
, Langberg, H., & Vatrapu, R. (2016).
Big social data analytics for public health: Facebook engagement and performance. I
2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016 [7749497] IEEE.
https://doi.org/10.1109/HealthCom.2016.7749497Vancouver
Straton N, Hansen K, Mukkamala RR, Hussain A, Grønli TM
, Langberg H o.a.
Big social data analytics for public health: Facebook engagement and performance. I 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016. IEEE. 2016. 7749497
https://doi.org/10.1109/HealthCom.2016.7749497Author
Straton, Nadiya ; Hansen, Kjeld ; Mukkamala, Raghava Rao ; Hussain, Abid ; Grønli, Tor Morten ; Langberg, Henning ; Vatrapu, Ravi. / Big social data analytics for public health : Facebook engagement and performance. 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016. IEEE, 2016.
Bibtex
@inproceedings{43dd8d5573be4677aefa51c8ff882974,
title = "Big social data analytics for public health: Facebook engagement and performance",
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.",
author = "Nadiya Straton and Kjeld Hansen and Mukkamala, {Raghava Rao} and Abid Hussain and Gr{\o}nli, {Tor Morten} and Henning Langberg and Ravi Vatrapu",
year = "2016",
doi = "10.1109/HealthCom.2016.7749497",
language = "English",
booktitle = "2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016",
publisher = "IEEE",
note = "18th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2016 ; Conference date: 14-09-2016 Through 17-09-2016",
}
RIS
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 -
