The Rules of Contagion: Why Things Spread - and Why They Stop

by Adam Kucharski

Although having meaningful face-to-face conversations can help change attitudes – as they have with prejudice and violence – viewing opinions in an online feed won’t necessarily have the same effect.

Social media researcher danah boyd (she styles her name as lower case) calls it ‘context collapse’.

According to boyd, underlying contexts can also change over time, particularly as people are growing up. ‘While teens’ content might be public, most of it is not meant to be read by all people across all time and all space,’ she wrote back in 2008.

Unlike real life, the interactions we have online are in effect on a stage.

Analysis of antisocial behaviour online has found that a whole range of people can become trolls, given the right circumstances. In particular, we are more likely to act like trolls when we are in a bad mood, or when others in the conversation are already trolling.[31]

By tweaking what people were exposed to, they found that emotion was contagious: people who saw fewer positive posts had on average posted less positive content themselves, and vice versa.

Looking at how design influences people’s behaviour is not necessarily unethical. Indeed, medical organisations regularly run randomised experiments to work out how to encourage healthy behaviour.

The experiment had shown that when a user saw fewer positive posts in their feed, the number of positive words in their status updates fell by an average of 0.1 per cent. Likewise, when there were fewer negative posts, negative words decreased by 0.07 per cent.

The study team argued that such differences were still relevant, given the size of the social network: ‘In early 2013, this would have corresponded to hundreds of thousands of emotion expressions in status updates per day.’ But some people remained unconvinced.

It turned out that articles that triggered an intense emotional response were more likely to be shared. This was the case both for positive emotions, such as awe, and negative ones like anger.

Berger and Milkman’s analysis found that having an element of surprise or practical value could also influence an article’s shareability.

On YouTube, for example, she suspected that the video recommendation algorithm might have been feeding unhealthy viewing appetites, pulling people further and further down the online rabbit hole.

‘Its algorithm seems to have concluded that people are drawn to content that is more extreme than what they started with – or to incendiary content in general,’ she wrote in 2018.[37]

In biology, this arms race is known as the ‘Red Queen effect’, after the character in Lewis Carroll’s Through the Looking-Glass. When Alice complains that running in the looking-glass world doesn’t take her anywhere new, the Red Queen replies that, ‘here, you see, it takes all the running you can do, to keep in the same place.’

When the Higgs rumours started, some users posted about the potential discovery, while others retweeted these comments to their own followers.

Most tweets didn’t go very far, only spreading the news to one or two others. But in the middle of the transmission network, there is a large chain of retweets, including two large-scale transmission events, with single users spreading the rumour to many other people.

Sometimes content will spread to lots of people from a single source – known in marketing as a ‘broadcast’ event – whereas on other occasions it will propagate from user to user.

The Stanford and Microsoft researchers found that broadcasts were a crucial part of the largest cascades.

The same is true of other online platforms: it’s extremely rare to get something that spreads, and even when it does, it doesn’t spread beyond a few generations of transmission. Most content just isn’t that contagious.[41]

Unless the reproduction number is zero, we should therefore expect to get some secondary cases occasionally.

Outbreak size = 1 + R + R2 + R3 + …

If R is between 0 and 1, the following equation is true: 1 + R + R2 + R3 + … = 1/(1–R)

For biological pathogens, a big concern is that these infections will adapt to their new hosts. During a small outbreak, viruses could pick up mutations that enable them to transmit more easily. The more people that get infected, the more chances for such adaptation.

Of the viruses that can spread among humans, the most successful tend to cause longer infections (i.e. larger duration) and spread directly from one person to another rather than via an intermediate source (i.e. more opportunities).[47]

When Facebook researcher Lada Adamic and her colleagues analysed the spread of memes on the social network, they noticed that content would often change over time.[49]

Some of these edits helped the meme propagate; when people included phrases like ‘post if you agree’, the meme was almost twice as likely to spread.

Remarkably, there were no examples of Facebook content that reached lots of friends and had a consistently high probability of spreading to each person that saw it.

This serves as a reminder of just how weak online outbreaks are compared to biological infections: even the most popular content on Facebook is ten times less contagious than measles can be.

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]

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’.

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.

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.

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.’

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.

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.

Again, this shows why it is important to spark multiple cascades, rather than trying to find a single ‘perfect’ tweet.

Although it’s very hard to predict whether a given book will take off in the first place, books that do become popular tend to follow a consistent pattern afterwards. The team found that most books on the bestseller list saw rapid initial growth in sales, peaking within about ten weeks of publication, which then declined to a very low level. On average, only 5 per cent of sales occurred after the first year.[58]

If we want to predict the shape of an outbreak, there are two things we really need to know: how many additional infections each case generates on average (i.e. the reproduction number), and the lag between one round of infection and the next (i.e. the generation time).

My outbreak simulations suggested that the neknomination trend wouldn’t last long. After a week or two, herd immunity would kick in, causing the outbreak to peak and begin to decline.

Although nominated-based games have tended to fade away after a few weeks, social media outbreaks don’t always disappear after their initial peak in popularity.

The team found that a big initial peak in interest made it less likely that the meme will appear again. ‘It is not the most popular cascades that recur the most,’ they noted, ‘but those that are only moderately popular’.

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 be...

The researchers actually found the opposite: it was generally people with fewer followers who spread the false news.

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]

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.

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.

‘When a measure becomes a target, it ceases to be a good measure’ as economist Charles Goodhart reportedly once said.[74]