transmission. In contrast, most online content won’t reach many
people unless there is some kind of mass broadcast event. According
to Peretti, marketing companies will often talk about things going
‘viral’ like a disease, but they actually just mean something has
become popular. ‘We were thinking in terms of an actual
epidemiological definition of viral, with a certain threshold of
contagion that results in it growing through time,’ as he once put it.
[54] ‘Instead of exponential decay, you get exponential growth. That
is what viral is.’
Most online cascades are not viral like pandemics are; they do not
grow exponentially. They are actually more like the stuttering
smallpox outbreaks that occurred in Europe during the 1970s. These
outbreaks would generally fade away, albeit with the occasional
superspreading event leading to a large cluster of cases. Yet the
smallpox superspreader analogy only goes so far, because media
outlets and celebrities have a reach far beyond what’s possible for
biological transmission. ‘A superspreader is someone who infects,
like, eleven people instead of two,’ Watts said. ‘You don’t have
superspreaders who infect eleven million people.’
G cascades aren’t the same as infectious
disease outbreaks, a traditional disease model won’t necessarily help
us predict what will happen online. But maybe we don’t need to rely
on biologically inspired predictions. Given the sheer volume of data
generated on social media, researchers are increasingly trying to
identify transmission patterns, and use these to predict the dynamics
of cascades.
How easy is it to predict online popularity? In 2016, Watts and his
colleagues at Microsoft Research compiled data on almost a billion
Twitter cascades.[55] They gathered data on the tweets themselves –
such as the time posted and topic – as well as information about the
users who initially tweeted them, such as their number of followers
and whether they had a history of getting a lot of retweets. Analysing
the resulting cascade sizes, they found that the content of the tweet
itself provides very little information about whether it would be
popular. As with their earlier analysis of influencers, the team found