Facebook and Public Health: A Study to Understand Facebook Post Performance with Organizations’ Strategy

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Abstrakt

This paper reports on a survey about the perceptions and practices of social media managers and experts in the area of public health. We have collected Facebook data from 153 public health care organizations and conducted a survey on them. 12% of organizations responded to the questionnaire. The survey results were combined with the findings from our previous work of applying clustering and supervised learning algorithms on big social data from the official Facebook walls of these organizations. In earlier research, we showed that the most successful strategy that leads to higher post engagement is visual content. In this paper, we investigated if organisations pursue this strategy or some other strategy that was successful and has not been uncovered by the machine learning algorithms. Performance of each organisation on Facebook is based on the number of posts (volume share) and the number of actions (value share). Calculation of performance with number of actions in the numerator and number of posts in the denominator reduces possible bias in the conclusions due to the varied size of organizations on social media. Moreover, our survey attempts to better understand the behaviour of organizations and to explain why almost half of the public health care content posted on Facebook is in the form of a short text message, where as the information can be communicated through seven other post types. Similar patterns and characteristics for different engagement clusters, also high and low performing companies suggests that a mixed-methods research approach consisting of machine learning techniques combined with expert knowledge using qualitative methods can offer important insights.
OriginalsprogEngelsk
TitelProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
RedaktørerJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Antal sider10
UdgivelsesstedLos Alamitos, CA
ForlagIEEE
Publikationsdato2017
Sider3123-3132
ISBN (Trykt)9781538627167
ISBN (Elektronisk)9781538627150, 9781538627143
DOI
StatusUdgivet - 2017
BegivenhedFifth IEEE International Conference on Big Data. IEEE BigData 2017 - Boston, USA
Varighed: 11 dec. 201714 dec. 2017
Konferencens nummer: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Konference

KonferenceFifth IEEE International Conference on Big Data. IEEE BigData 2017
Nummer5
LandUSA
ByBoston
Periode11/12/201714/12/2017
Internetadresse

Emneord

  • Big social data
  • Social media performance
  • Social media strategy
  • Facebook data

Citationsformater