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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings

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.

Publication information

Original languageEnglish
Title of host publicationProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
EditorsJian-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
Number of pages10
Place of PublicationLos Alamitos, CA
PublisherIEEE
Publication date2017
Pages3123-3132
ISBN (Print)9781538627167
ISBN (Electronic)9781538627150, 9781538627143
DOIs
StatePublished - 2017
Event5th IEEE International Conference on Big Data. 2017 - Boston, United States
Duration: 11 Dec 201714 Dec 2017
Conference number: 5
http://cci.drexel.edu/bigdata/bigdata2017/

Conference

Conference5th IEEE International Conference on Big Data. 2017
Nummer5
LandUnited States
ByBoston
Periode11/12/201714/12/2017
Internetadresse

Bibliographical note

CBS Library does not have access to the material

    Research areas

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

ID: 55433586