In an attention economy, there are distinct incentives to leverage everything at your disposal to stimulate user engagement. It is a straight forward equation: To sell ads, you need eyeballs. To get eyeballs, you need attention.
To put it another way: attention equals good analytics… and good analytics equals cash.
Thus, to optimize engagement, social media corporations build content algorithmic distribution models that (inadvertently or not) seem to foster ‘echo chambers’ and ‘flame wars.’ Some call these designs mobocratic algorithms. Ultimately, the driving economic incentive of social media is to garner a user reaction — and nothing galvanizes us to furiously click and comment on things more than directly confirming our biases or assaulting them.
The ‘mobocratic algorithm’ hypothesis suggests that the growing polarization and ideological battles we see online are, at least in part, facilitated by the technical design of these platforms, which are in turn reciprocally animated by the commercial imperative to harness user attention to generate advertising revenue.
(Image credit: Nathan W. Pyle via buzzfeed.com)
So how can we invert the U-shaped distribution into a normal distribution, where productive conversations represent the norm, rather than the exception?
(Image credit: Canada Conversations Design Hackathon initial framework webinar.)
I keep coming back to this conundrum: it seems like the more data we have to sift, decipher, and filter, the more incentive there is for someone to be as loud and as obnoxious as possible to win our attention.