Big Social Data Analytics for Public Health: Facebook Engagement and Performance

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    In recent years, social media has offered new opportunitiesfor interaction and distribution of public healthinformation within and across organisations. In this paper, weanalysed data from Facebook walls of 153 public organisationsusing unsupervised machine learning techniques to understandthe characteristics of user engagement and post performance.Our analysis indicates an increasing trend of user engagement onpublic health posts during recent years. Based on the clusteringresults, our analysis shows that Photo and Link type postsare most favourable for high and medium user engagementrespectively.
    In recent years, social media has offered new opportunitiesfor interaction and distribution of public healthinformation within and across organisations. In this paper, weanalysed data from Facebook walls of 153 public organisationsusing unsupervised machine learning techniques to understandthe characteristics of user engagement and post performance.Our analysis indicates an increasing trend of user engagement onpublic health posts during recent years. Based on the clusteringresults, our analysis shows that Photo and Link type postsare most favourable for high and medium user engagementrespectively.
    LanguageEnglish
    Title of host publication2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)
    EditorsAlois Paulin
    Number of pages6
    Place of PublicationLos Alamitos, CA
    PublisherIEEE
    Date2016
    Pages442-447
    Article number7749497
    ISBN (Electronic)9781509033706, 9781509033713
    DOIs
    StatePublished - 2016
    EventIEEE HealthCom 16: 18th International Conference on E-health, Networking, Application & Services - BMW Welt, Munich, Germany
    Duration: 14 Sep 201617 Sep 2016
    Conference number: 18
    http://ieeehealthcom2016.com/

    Conference

    ConferenceIEEE HealthCom 16
    Number18
    LocationBMW Welt
    CountryGermany
    CityMunich
    Period14/09/201617/09/2016
    Internet address

    Cite this

    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. In A. Paulin (Ed.), 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 442-447). [7749497] Los Alamitos, CA: IEEE. DOI: 10.1109/HealthCom.2016.7749497
    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). editor / Alois Paulin. Los Alamitos, CA : IEEE, 2016. pp. 442-447
    @inproceedings{4c9eab06140e4ac4824d1ce275deb574,
    title = "Big Social Data Analytics for Public Health: Facebook Engagement and Performance",
    abstract = "In recent years, social media has offered new opportunitiesfor interaction and distribution of public healthinformation within and across organisations. In this paper, weanalysed data from Facebook walls of 153 public organisationsusing unsupervised machine learning techniques to understandthe characteristics of user engagement and post performance.Our analysis indicates an increasing trend of user engagement onpublic health posts during recent years. Based on the clusteringresults, our analysis shows that Photo and Link type postsare most favourable for high and medium user engagementrespectively.",
    author = "Nadiya Straton and Kjeld Hansen and Mukkamala, {Raghava Rao} and Abid Hussain and Tor-Morten Gr{\o}nli and Henning Langberg and Ravi Vatrapu",
    year = "2016",
    doi = "10.1109/HealthCom.2016.7749497",
    language = "English",
    pages = "442--447",
    editor = "Alois Paulin",
    booktitle = "2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)",
    publisher = "IEEE",
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    }

    Straton, N, Hansen, K, Mukkamala, RR, Hussain, A, Grønli, T-M, Langberg, H & Vatrapu, R 2016, Big Social Data Analytics for Public Health: Facebook Engagement and Performance. in A Paulin (ed.), 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)., 7749497, IEEE, Los Alamitos, CA, pp. 442-447, Munich, Germany, 14/09/2016. DOI: 10.1109/HealthCom.2016.7749497

    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). ed. / Alois Paulin. Los Alamitos, CA : IEEE, 2016. p. 442-447 7749497.

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    TY - GEN

    T1 - Big Social Data Analytics for Public Health

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    AU - Straton,Nadiya

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    AU - Vatrapu,Ravi

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    N2 - In recent years, social media has offered new opportunitiesfor interaction and distribution of public healthinformation within and across organisations. In this paper, weanalysed data from Facebook walls of 153 public organisationsusing unsupervised machine learning techniques to understandthe characteristics of user engagement and post performance.Our analysis indicates an increasing trend of user engagement onpublic health posts during recent years. Based on the clusteringresults, our analysis shows that Photo and Link type postsare most favourable for high and medium user engagementrespectively.

    AB - In recent years, social media has offered new opportunitiesfor interaction and distribution of public healthinformation within and across organisations. In this paper, weanalysed data from Facebook walls of 153 public organisationsusing unsupervised machine learning techniques to understandthe characteristics of user engagement and post performance.Our analysis indicates an increasing trend of user engagement onpublic health posts during recent years. Based on the clusteringresults, our analysis shows that Photo and Link type postsare most favourable for high and medium user engagementrespectively.

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    DO - 10.1109/HealthCom.2016.7749497

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    Straton N, Hansen K, Mukkamala RR, Hussain A, Grønli T-M, Langberg H et al. Big Social Data Analytics for Public Health: Facebook Engagement and Performance. In Paulin A, editor, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom). Los Alamitos, CA: IEEE. 2016. p. 442-447. 7749497. Available from, DOI: 10.1109/HealthCom.2016.7749497