This is the latest in a series of blogs exploring the use of social media in public health and healthcare. The blogs have used social network analysis to study awareness raising campaigns (#VaccinesWork, National Clean Air Day, Antibiotic Awareness Week 2016), conferences (European Public Health conference 2016, Quality2017), and key influencers (exploring whether the 85:3% rule applies to tweeting about health and healthcare).
You can also read and download the blog in a PDF.
This blog attempts to share some key methodological pitfalls in planning, conducting and sharing the results of a social network analysis. It is structured around 5 main ideas:
- Finding a needle in a haystack
- Filtering out the minnows and sticklebacks
- Working out the size of pond for the big fish
- Slicing out the spam
- The ones that got away – and how to include them in the final analysis
The work has reminded me of work on cell culture when I was a medical student: I worked in a lab in Dallas, Texas, for 4 months November 1994-February 1995, studying adrenal tumour cells. This was my first time working abroad, and I was a little star struck, working in Parkland Hospital, famous as the hospital that treated JFK on 22 November 1963, and home to Nobel Prize winners including Alfred Gilman. My research did not reach such heights. Results were disappointing and unpredictable, week after week, and I was running out of time. Research is marked as much by its “failures” as its “successes”; both are an essential part of learning, though the stumbles are shared less than the leaps forward.
Continue reading “Social network analysis: quirks, pitfalls and biases” →