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

Nadiya Straton; Kjeld Hansen; Raghava Rao Mukkamala; Abid Hussain; Tor Morten Grønli; Henning Langberg; Ravi Vatrapu

doi:10.1109/HealthCom.2016.7749497

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

Nadiya Straton, Kjeld Hansen, Raghava Rao Mukkamala, Abid Hussain, Tor Morten Grønli, Henning Langberg, Ravi Vatrapu

Section of Social Medicine

11 Citations (Scopus)

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
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	https://doi.org/10.1109/HealthCom.2016.7749497
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

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Big social data analytics for public health : Facebook engagement and performance. / Straton, Nadiya; Hansen, Kjeld; Mukkamala, Raghava Rao et al.

2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016. IEEE, 2016. 7749497.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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. in 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, Germany, 14/09/2016. https://doi.org/10.1109/HealthCom.2016.7749497

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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",

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AU - Grønli, Tor Morten

AU - Langberg, Henning

AU - Vatrapu, Ravi

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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.

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M3 - Article in proceedings

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PB - IEEE

Y2 - 14 September 2016 through 17 September 2016

ER -

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

Abstract

Conference

UN SDGs

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