It’s perhaps fair to say that there is slightly less attention (among academia at least) into making content go viral on social media.  A few years ago a number of studies came out exploring the kind of factors involved in viral tweets, from the subject line to the time of post.  That’s not to say that interest has completely dried up however, as a recent study from Beihang University in China shows.

It shows that the virality of a tweet can be accurately gauged from the first 50 retweets (which I suspect for many of us might be regarded as viral in itself!).  The authors arrived at this conclusion having studied around one months worth of Twitter data, consisting of over 12 million tweets and 1.5 million retweets.

They developed a model that was attempted to predict the virality of a tweet from the first 50 retweets associated with it, taking into account the speed of retweets, the kind of accounts that were sharing the content, and so on.

The model was tested via a number of simulations using real data and found it to be more effective at predicting the viral nature of content than other models that rely on factors such as social reinforcement and trapping effects.  Both for simulated and real-world data, the model was outperforming standard community models, although as is so often the case, when the community model and the infectivity model developed by the team were combined, the best results of all were achieved.

“We propose a simulation model using Twitter data to show that infectivity, which reflects the intrinsic interestingness of an information cascade, can substantively improve the predictability of viral cascades,” the authors conclude.