Understanding ‘normal’ is important, too.
In recent years, I’ve spent a lot of time expanding on how classic media logic differs from network media logic. For anyone working with the media, it’s important to understand how the technological shift has affected us all. In times of change, it’s easy to focus on the change itself; some would even argue that it’s part of human nature to pay closer attention to whatever deviates from the normal.
However, we shouldn’t allow media (or ourselves) to portray normality as a matter of less importance in a democratic society.
The Normal Distribution
Most Doctor Spin readers are familiar with the normal distribution curve — popularly known as the Bell Curve. For many phenomenons, you’re more likely to find a variable closer to the mean — and less likely to find them out on fringes. The normal distribution is found to be prevalent not only in mathematics, but also in our everyday society, ranging from the most diverse of areas.
Therefore, it’s only fair to assume that we, as human beings, are more interested in outliers far out on either side of the spectrum. Human behaviour is a self-regulating system operating on negative or positive feedback. If our system fail to recognise positive feedback, we’re still fine. But failing to recognise negative feedback will quickly get us doomed. We’re synaptically hardwired to pay closer attention to anything out of the ordinary.
Media Interest Distribution
The media is built around the organising principle of human attention, and as such, it’s only natural for content creators and media owners to focus on the far ends of the normal distribution curve.
Such circumstances have always posed a challenging dilemma for journalists:
How can mass media objectively report on the state of society when the audience can’t be bothered about the middle of the curve?
Instead of solving the actual problem, journalism found a way around it — “balanced reporting”. For every report on certain outlier phenomenons, events, groups, or individuals, “objective media” strives to balance these reports by finding outliers on the opposite side of the normal distribution. The result is a portrayal of the world as “hyper-realistic”.
The problem with this approach is that the sum of opposite outliers rarely results in anything that resembles the mean. For instance, if the media reports on vocal and aggressive climate alarmists on the one side, and equally vocal and aggressive climate deniers on the other side, the media portrays a polarised debate far from consensus. But the truth could be that a staggering majority of the population is more than ready to co-operate just as soon as “everyone” stops fighting and suggests viable solutions instead.
This effect hasn’t diminished as a result of increased social interconnectedness; it has rather been amplified. By operating on the same basic idea, the principle of human attention, most social media algorithms operate in much the same way. There’s not enough research on the subject to be certain; we don’t yet know just how much we are amplifying our communicative behaviours to get social feedback from our social peer groups.
In the media landscape of hyper-realistic reporting, my personal observation is this:
I often hear people accusing other people of not understanding outlier phenomenons, events, groups, or individuals. But I would argue that it’s far more risky to loose touch with the general averages of society. Today, people seem to know far more about what the climate alarmists and deniers are saying than what they know about everyday life outside their own social class.