It is almost 50 years ago since “Our World”, the first live international satellite TV broadcast, most famous for the first performance of The Beatles’ “All You Need Is Love” (25 June 1967). The show reached an audience of 400-700 million. The most famous band on earth beamed out to living rooms across the world. This was an impressive achievement, but the information flowed just one way. The digital revolution was yet to happen.
Between 24 and 31 April this year Immunization Week tweets using the #VaccinesWork hashtag passed across devices almost 1.5 billion times(1). Information flowed in both directions – international and national health organisations promoting vaccination, and individuals responding and sharing information of their own. The overall impression of Immunization Week is an extremely well planned and organised multi-agency international campaign, with plenty of evidence-based tweets using images and links to informative webpages. There were tweets by national organisations that provided country specific information. There was also a considerable amount of high quality and informative tweeting at individual level, by clinicians, parents and many others, though these posts risked being overwhelmed by tweets from international organisations in the “big data” analysis. While there was some negative tweeting by anti-vaccination campaigners, some of them with considerable reach on social media, the balance overall was firmly in favour of vaccination.
This blog summarises the main findings of the big data analysis, pulls out some detail (eg top tweets and resources, the type of influence exerted by top tweeters), and describes the methodology (basic and advanced) so that others can repeat this type of analysis on other global health campaigns. The big data techniques include NodeXL maps, similar in appearance to the spread of communicable diseases (figure 1), except in social media analysis spread is usually seen as a positive. I have used two Public Health evaluation frameworks to summarise main findings (Donabedian’s Structure, Process and Outcome; and RE-AIM).