Internet Marketing expert Dr Brent Coker claims to have developed an algorithm that can predict which ad movies will go viral on YouTube. I don’t plan a career move to advertising but was nevertheless intrigued by the claim from a research methods & design perspective. Unfortunately, there is very little information available (yet?) and what information is available makes me a bit skeptical about the reliability of the conclusion. Still, Dr Coker’s approach might make for a nice discussion in the context of a Research Design course since it touches upon a question students can relate to, and raises various issues from operationalization to theory specification to theory testing.
In short, according to Dr Coker, “there are four elements that need to be in place for a branded movie to become viral: (1) congruency, (2) emotive strength, (3) network-involvement ratio, and (4) paired meme synergy”. Congruency is the consistency of the video’s theme with brand knowledge. Disgust and fear, for example, imply powerful emotive strength. The network-involvement ratio refers to how relevant the message is to the seeded network. The last element ‘paired meme synergy’ means that certain memes are effective when paired with certain other memes. “For example, impromptu entertainment acts appeared to work when paired with ‘Eyes Surprise’. When paired with ‘bubblegum nostalgia’, the … pair doesn’t work. Anticipation works with Voyeur, but not on its own. And so forth.”
As I said, there is not much information available on the research design, but from what I can gather, the predictive algorithm is based on an inductive approach: analyze movies that did go viral and see what their characteristics are. Such an approach would be OK to generate ideas, but one should be careful overselling the inductively-identified “solution” as a predictive algorithm which has been properly tested. An obvious next step would be to see whether the “solution” predicts outside the sample it was derived from, and maybe Dr Coker is working on that stage now. I wonder, however, whether the rather flexible definitions of some of the predictive elements make a testing of the approach feasible even in principle. It seem hard to identify the ‘network-involvement ration’, for example, prior to observing the outcome. The meme-pairing idea is interesting, but again: if there is no clear idea why certain memes should go together, there is a high risk of the analysis just playing catch-up with the data.
For example, how would you score this awesome recent viral video (my take would be Disruption Destruction + Performance + Skill Bill + Simulation Trigger, for the list of possible memes see here)?:
P.S. On a somwhat related note: The Atlantic has a feature on the rise of big data which says that Google runs ”100-200 experiments on any given day, as they test new products and services, new algorithms and alternative designs”.