Mapping power law distributions in digital health social networks: Methods, interpretations, and practical implications
Open accessAbstract
BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.
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RCMP Cadet participation in daily surveys does not differ from the general population.
“RCMP Cadet participation does not differ from the general population”
From Trouble with the curve: the 90-9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training (2024) · cites this paper as [42]
The Methods (Overview) and Results examine posting in four digital health social networks, not RCMP Cadets or daily surveys. Searches for RCMP Cadets, daily surveys, and a comparison with the general population found no passage addressing the claim. (LLM, conf=0.78)
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- Authors
- van Mierlo, T., Hyatt, D., & Ching, A. T.
- Affiliations
- University of Toronto (CA)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2015-06-25
- Volume
- 17
- Issue
- 6
- Pages
- e160
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4526963
- OpenAlex
- W1624537879
- Cited by
- 22
- References
- 45
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 1
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CentAUR (University of Reading) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Toronto
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- power-law distribution, frequency distribution, right-skewed distributions, superusers, network structure, panic disorder, smoking cessation, online health communities
- MeSH terms
- Depressive Disorder, Humans, Power, Psychological, Smoking, Social Behavior, Social Support, Smoking Cessation, Panic Disorder, Telemedicine, Alcohol-Related Disorders, Internet, Social Networking
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