Social media, perhaps better named antisocial media, is a tangled mess of ideas that spread out around the globe, boosted by each media company’s algorithm and users.
Not surprisingly, the most extreme ideas spread the farthest and fastest. It is human nature to be interested in extraordinary things, and I use the term extraordinary in the true sense of the word. We spend time on things that catch our attention, while ignoring things that don’t.
We slow down and look when there has been an accident on the roadway. The news media has an expression, “If it bleeds, it leads.” If it is a story about some horrific accident or shooting, etc., it is the main story, especially if they have the footage.
We are not interested in the usual. Stories about everyday life are dull, just meh! A social-media stream with threads about a daily routine — going shopping, feeding the cat, brushing teeth, going to bed — will not catch fire and go viral.
But an extreme or obviously ridiculous view will be commented on and recirculated by those who agree, whose biases are confirmed, and within that network of people with similar views, the original post is commented on and reposted and on and on it goes. And those who disagree get angry, comment, and repost; they give it oxygen and on and on it goes. The more extreme the view, the greater the bile and anger; the wider the original and subsequent posts are spread.
Social-media companies are very happy with this state of affairs. The more eyeballs, the greater the advertising revenue. Their algorithms will amplify threads that catch people’s attention. They understand that social media, as they have constructed it, is like a neural network; the pathways between each subscriber account are strengthened the more times it is used.
If you are constantly viewing, “liking,” commenting on, and/or reposting a certain person’s posts, the pathway between you and that person is strengthened and you will see more of that person’s posts at the top of your feed.
Understanding how these connections work and what this means is extremely valuable. These networks are a very rich source of data that can be used in both positive and negative ways. Constructed in a certain way and planted with the right people, stories will spread to those with similar views, appealing to their confirmation biases. Some if not most will accept the story at face value and repost, again to likeminded people. In this way, stories are like genes: Those that resonate are passed on and those that don’t wither on the vine.
Social-media influencers use the same technique. Their posts can be an extremely powerful tool to encourage people to buy a certain piece of clothing or visit a particular restaurant.
Humans queue behind one another. If a restaurant is busy, we want to go in, while if it is empty, we stay away. Birds do the same thing. If there are a lot of birds in one area, other birds seeing the mass will join them. This is an evolutionary trait; birds flock where the food is and so a flock of birds indicates food to other birds.
Someone who understands this is András Vicsek, a Hungarian organisational psychologist. At an early age, he realised how important different roles in a community can be.
Hungary has a tradition of plotting out social connections among people, though not where you might expect. It was done in school classrooms. Who spoke to whom? Who was in the friend groups? Who were the dominant children? What cliques had formed? Who was in and who was out? Vicsek believed that that this sort of research could be extremely useful if the same techniques were applied to businesses.
He tells a story of how, early in his career, he was asked to visit a tile-manufacturing business with a headquarters in Budapest and two manufacturing facilities outside the city. The company was planning to introduce new robotic technology that would make each plant more productive. Employees became anxious about their future, fearing job losses. As a consequence, misinformation spread and morale suffered. No matter what management did, they could not improve morale or stop the misinformation from spreading.
András set about questioning the staff at the various business units, not as you might expect, but about who they liked, admired, met socially, went to for advice, and the like.
Using a network visualisation graph that plotted all of these connections, he was surprised to see that no line manager was strongly connected to the manufacturing staff. Managers were highly connected to themselves, but not to the rest of the employees; the management-to-shop-floor interconnectedness was very weak. This meant that managers spoke amongst themselves, but did not speak (or listen) to those working at the “coal face”. The managers’ influence was extremely limited.
The graph also showed that one person was highly connected, not only to all of the manufacturing staff, but also those at the head office. He was the health and safety officer for the company and as part of his job, he visited all three sites on a regular basis, speaking to everyone about aspects of health and safety and passing on gossip at the same time. He was responsible for a lot of the misinformation.
You might think that senior management took a dim view and fired him when it was pointed out to them. Instead, they promoted him, gave him a pay rise, and made sure that he passed on the messages that managers wanted passing on.
A speeded-up visualisation of customers on a casino floor may look like a group people randomly visiting various table games and slot machines, but each person is making a decision, either passively or purposefully, about where to stop and play.
Sometimes they notice certain slot machines that are very popular, but with no free places, and make a mental note to try one when the machines become free. Others perhaps have listened to one of their friends who has told them about a certain “hot” machine on the floor and are trying to find it. Patrons on the casino floor are a group of individuals, but subject to the same currents and pressures as any other “social network”.
These networks are too big to track and plot manually. However, hidden within player data are patterns of behaviour that can be plotted and analysed. When facial recognition is used to see how individuals move and react on a casino floor and is combined with player table game and slot data, artificial intelligence systems will be able to plot who are the real influencers. It may be that your most important customers are not those who spend the most money.
It is clear that certain individuals have outsized influence on others, whether in schools, businesses, entertainment, politics, sports, and life in general. Knowing who those people are, whom they can influence, and what the key messages that will resonate throughout their networks are will be very valuable information indeed.
