I hadn’t expected the Royal Institution talk to get so much attention, but what really came as a surprise was how the views had accumulated. For its first year online, the video had gained relatively little interest, getting a hundred or so views per day. Then suddenly, in the space of a few days, it picked up more attention than it had in an entire year.
Number of YouTube views per day for my 2016 Royal Institution talk
Data: Royal Institution
Perhaps people had started sharing it online, making it go viral? Looking at the data, the real explanation was much simpler: the video had been featured on the YouTube homepage. As the views spiked, the YouTube algorithm added it to the ‘suggested video’ lists that appear alongside popular videos. Almost 90 per cent of people who viewed the talk found it on the homepage or one of these lists. It was a classic broadcast event, with one source generating almost all of the views. And once the video was popular, its popularity created a feedback effect, attracting even more interest. It shows how much the video benefitted from online amplification, first by the Royal Institution to get those initial few thousand views, then by the YouTube algorithm to deliver a much bigger audience.
There are three main types of popularity on YouTube. The first is where videos get a consistent, low-level number of views. This number randomly fluctuates from day-to-day, without noticeably increasing or decreasing. Around 90 per cent of YouTube videos follow this pattern. The second type of popularity is when a video suddenly gets featured on the website, perhaps in response to a news event. In this situation, almost all of the activity comes after the initial peak. The third type of popularity occurs when a video is being shared elsewhere online, gradually accumulating views before peaking and declining again. It’s also possible to observe a mixture of these shapes; a shared video may get a boost by being featured then settle back down to a low level, like mine did.[66]
Video is a particularly persistent form of media, with interest tending to last much longer than for news articles. A typical social media news cycle is around two days; in the first twenty-four hours, most content comes in the form of articles, with shares and comments following afterwards.[67] However, not all news is the same. Researchers at MIT have found that false news tends to spread further and faster than true news. Maybe this is because high-profile people with lots of followers are more likely to spread falsehoods? The researchers actually found the opposite: it was generally people with fewer followers who spread the false news. If we think of contagion in terms of the four DOTS, this suggests false information spreads because the transmission probability is high, rather than there being more opportunities for spread. The reason for the high transmission probability? Novelty might have something to do with it: people like to share information that’s new, and false news is generally more novel than true news.[68]
It’s not just about novelty, though. To understand how things spread online, we also need to think about social reinforcement. And that means taking another look at the concept of complex contagion: sometimes we need to be exposed to an idea multiple times before we adopt it online. For example, there’s evidence that we’ll share memes online without much prompting, but won’t share political content until we see several other people doing so. When Facebook users changed their profile picture to a ‘=’ symbol in support of marriage equality in early 2013, on average they only did so once eight of their friends had. Complex contagion also influenced the initial adoption of many online platforms, including Facebook, Twitter and Skype.[69]
A quirk of complex contagion is that it spreads best in tight-knit communities. If people share lots of friends, it creates the multiple exposures needed for an idea to catch on. However, such ideas may then struggle to break out and spread more widely.[70] According to Damon Centola, the structure of online networks can therefore act as a barrier to complex contagion.[71] Many of our contacts online will be acquaintances rather than part of a closely linked friendship group. Whereas we might adopt a political stance if lots of our friends do, we’re less likely to pick it up from a single source.
This means that complex contagion – such as nuanced political views – can have a major disadvantage on the internet. Rather than encouraging users to develop challenging, socially complex ideas, the structure of online social interactions instead favours simple, easy-to-digest content. So perhaps it’s not surprising that this is what people are choosing to produce.
With the rising availability of data in the early twenty-first century, some suggested that researchers would no longer need to pursue explanations for human behaviour. One of them was Chris Anderson, then Wired editor, who in 2008 famously penned an article proclaiming the ‘end of theory’. ‘Who knows why people do what they do?’ he wrote. ‘The point is they do it, and we can track and measure it with unprecedented fidelity.’[72]
We now have vast quantities of data on human activity; it’s been estimated that the amount of digital information in the world is doubling every couple of years, with much of it generated online.[73] Even so, there are a lot of things we still struggle to measure. Take those studies of obesity or smoking contagion, which show just how difficult it can be to pick apart transmission processes. Our inability to measure behaviour isn’t the only problem. In a world of clicks and shares, it turns out we’re not always measuring what we think we’re measuring.
At first glance, clicks seem like a
