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
Publication date2017
StatePublished - 2017
Event2017 IEEE International Conference on Big Data - Boston, United States
Duration: 11 Dec 201714 Dec 2017


Conference2017 IEEE International Conference on Big Data
CountryUnited States
Internet address

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: 55360441