The outlook is even worse for a typical marketing campaign. Although Jonah Peretti once bet that it was possible to get something to deliberately take off, he’s since acknowledged that it’s much harder to guarantee contagion when working to a client brief.[51] Consider the difference between his original Nike e-mail, which spread widely, and those later e-mail campaigns, which were far less transmissible. Peretti and Watts have pointed out that infectious diseases have millennia of evolution on their side; marketers don’t have nearly as much time. ‘The chances are, therefore, that even talented creatives will typically design products that exhibit R less than 1, no matter how hard they try,’ they suggested.[52]
Fortunately, there is another way to increase the size of an outbreak: get the message out to more people at the start. In the above examples, we’ve been analysing stuttering outbreaks by assuming that one person is infectious at the start. If the reproduction number is small, this will lead to a small outbreak that fades away quickly. One way to fix this is to simply introduce more infections. Peretti and Watts call it ‘big seed marketing’. If we get a slightly contagious message to lots of people, it can pick up additional attention during subsequent small outbreaks. For example, if we send a non-contagious message to one thousand people, we’ll reach one thousand people. If instead we launch a message with an R of 0.8, we’d expect to reach five thousand people in total. Much of BuzzFeed’s early content became popular in this way. People saw articles on the website, then shared them with a handful of friends on sites like Facebook. Having pioneered the idea of ‘reblogging’ in the early 2000s, Peretti’s team took full advantage of it in the decade that followed. By 2013, Buzzfeed had been named the most ‘social’ publisher on Facebook, with more comments, likes, and shares than any other organisation.[53] (Huffington Post, Peretti’s former company, was second.)
If web content generally has a low R and needs multiple introductions to spread, it suggests that we shouldn’t be thinking about online contagion as if it’s the 1918 flu virus or sars. Infections like pandemic flu spread easily from person to person, which means outbreaks initially grow larger and larger over several generations of 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.’
Given that social media 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 that a user’s past tweeting success was far more important. Even so, their overall prediction ability was fairly limited. Despite having the sort of dataset a disease researcher could only dream of, the team could explain less than half the variability in cascade size.
So what explained to the other half? The researchers acknowledged that there might be some additional, as-yet-unknown features of success that could improve prediction ability. However, a large amount of the variation in popularity will depend on randomness. Even if we have detailed data about what is being tweeted and who is tweeting it, the success of a single post will inevitably depend a lot on luck. Again, this shows why it is important to spark multiple cascades, rather than trying to find a single ‘perfect’ tweet.
Because it’s so difficult to predict a tweet’s popularity before it’s been posted, an alternative is to wait and look at the start of the cascade before making a prediction. This is known as the ‘peeking method’, because we’re looking at data on the early spread before we predict what will happen next.[56] When Justin Cheng and his colleagues analysed sharing of photos on Facebook in 2014, they found that their predictions got much better once they had some data on the initial cascade dynamics. Large cascades tended to show broadcast-like spread early on, picking up lots of attention quickly.
