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Change with Growth/Data Engineering
We become more productive and resilient when exposed to uncomfortable ideas, and sometimes we fall back into old habits. As it turns out, we can use a big database and social network analysis to understand why some groups and ideas become contagious, and other times they don’t.
In this story, I’ll discuss how network analysis can help us understand why some ideas go viral and some don’t. I’ll also discuss whether network science can predict virality for new products and ideas.
1. What is Virality?
Viral ideas are solid, evidence-based ideas that spread rapidly from person to person. Faced with an epidemic or a crisis, we need strong ideas, not just quick fixes.
Virality means something different depending on context. In scientific contexts, we talk about the “number of virus particles” in a population of people. For example, if scientists compare 10 patients with a particular disease to 10 healthy people, the number of virus particles would be the number of patients with the disease divided by the number of people with both the healthy and the disease. So, for example, if the virality is 10% for a disease, 10% of the people with the disease will be infected. Of course, with an epidemic, the virality can be 0%!
