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Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications.

van Mierlo T, Hyatt D, Ching AT - J. Med. Internet Res. (2015)

Bottom Line: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research Associate, Henley Business School, University of Reading, Oxfordshire, United Kingdom. tvanmierlo@evolutionhs.com.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

DHSN actor ranking and power curve ranking with trendline and R2 value.
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figure6: DHSN actor ranking and power curve ranking with trendline and R2 value.

Mentions: When logged, each of the DHSN’s rank and post frequency data closely resembled power distributions. When Excel power trend lines were added, R2 values indicated that to a very high degree, the variance in post frequencies is explained by actor rank (Figure 6).


Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications.

van Mierlo T, Hyatt D, Ching AT - J. Med. Internet Res. (2015)

DHSN actor ranking and power curve ranking with trendline and R2 value.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526963&req=5

figure6: DHSN actor ranking and power curve ranking with trendline and R2 value.
Mentions: When logged, each of the DHSN’s rank and post frequency data closely resembled power distributions. When Excel power trend lines were added, R2 values indicated that to a very high degree, the variance in post frequencies is explained by actor rank (Figure 6).

Bottom Line: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Research Associate, Henley Business School, University of Reading, Oxfordshire, United Kingdom. tvanmierlo@evolutionhs.com.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus