- Title
- Social media analytics, learning analytics and healthcare industry : risky drinking
- Creator
- Ahmed, Ashir; Martin, Jennifer; McKay, Elspeth; Towl, David; Haussegger, Zac
- Date
- 2022
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186609
- Identifier
- vital:16903
- Identifier
-
https://doi.org/10.1007/978-3-030-86316-6_5
- Identifier
- ISBN:2190-3018 (ISSN)
- Abstract
- Much is written about social media analytics, learning analytics and health care industry research projects. However, it is rare to find all three domains in the same study, as each discipline is usually kept separate from another. The concern with this isolation approach is the probability of missing essential synergies, leading to a combined effect more significant than the sum of their particular products. Social media analytics may uncover key social media search terms that reflect the language used by groups of people engaging in risky drinking behaviour. At the same time, the view of behaviour through a psychometric testing lens may reveal other things. This chapter describes both social media analytics and learning analytics from the perspective of the health care industry. The chapter explains the intersection of social media analytics and learning analytics; in the first instance, the Rasch measurement model is used to discover the probability of agreement relating to human behaviour issues; a case study amplifies health services provision outcomes. The results from the social media analytics revealed that groups’ risky drinking behaviour data collected by Talkwalker showed linguistic variations with the double meanings of words used, which affected results. The learning analytics of alcohol consumption included a post-hoc comparison using a Bonferroni correction t-test which revealed the mean of never-married people was significantly higher than married people. The significance of these findings demonstrates that data analysis should be open to using a mixture of data analytics tools to reach a finer-grained interpretation to model other complex problems. © 2022, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Manage Your Own Learning Analytics : Implement a Rasch Modelling Approach Chapter 5 p. 113-136
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © Springer Nature Switzerland AG 2022
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