![]() ![]() In the past, we got our information from one place – TV, magazines, newspapers, independent websites – and we socialized elsewhere. Such a design can naturally be vulnerable to those in power who’d like to cleanly segment society for their own interests. ![]() But when people have such a hard-coded, precise way of trimming their network of friends through a digital platform, and when that friend network is also one’s primary source of information, it truly devolves into spiteful tribalism. It’s perhaps natural behavior to connect with like-minded people, and avoid those with different beliefs. It is not uncommon for people to unfollow friends because of a difference in beliefs. These connections are made stronger because of an algorithm that favors content that is triggering, reinforcing one group’s biases and making the other group completely wrong. Which is why I call it the hype machine, algorithms that favor salacious content, false content, you know, things that are blood-boiling.” Clustered groups, no common groundĪccording to Aral’s explanations, the way we grow our networks, it’s already biased towards people that are already near our own beliefs. “That combined with the business model and the algorithmic amplification of engagement where I am trying to get people really to share a lot, to comment a lot and to just get riled up. “But if I do recommend to you friends of friends or I disproportionately recommend Friends of friends, it’s going to enhance and amplify the clustering of society into these tightly knit communities that are so far apart from each other but are much more densely connected within,” Aral added. People are not straying far from their original social circles, which may result in a shortage of perspective. While this is an easy fix for social networks, it has also resulted in unhealthy segregation. So it’s an easy engineering shortcut to make those algorithms run more smoothly and efficiently at your friends of friends.” It’s difficult to search the billions of people on social media to recommend someone to you. I need to find people to recommend to you to connect with. “Well, in friends suggestion algorithms which vastly are responsible for the connections that we make on LinkedIn, Facebook, Twitter and the rest are – it’s a hard problem. One of the design features that has enabled this devolution into digital tribes is the “friends of friends” growth design.Īral explains what this means. On a more intimate level, isn’t uncommon for people now to trim their contacts list down to only those they agree with, creating what has been called an echo chamber, and separating people into tribes with spears at the ready. If you look at charts plotting political affiliation now, it appears like two peaks with a long valley separating it, whereas in the past, it may have looked more like a plateau. It’s either you’re team blue or red in the era of the hype machine. In a Rappler Talk episode with Rappler executive editor Maria Ressa, Aral discusses the book, including how the hype machine’s design has led to deep polarization that has effectively erased the important middle ground that has, up until this point, allowed society to function with civility. The hype machine refers to how the current design of social networks catalyzes the spread of trends and false, emotion-stirring content on social media, which has been detrimental to mostly everything except the bottom line of social media companies and politicians aware of the machine’s design and how to exploit it. The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health–and How We Must Adapt came out on Tuesday, September 15. A new book by Sinan Aral, the Massachusetts Institute of Technology’s director of the initiative on the digital economy, explores the neuroscience behind social networks, the “levers” that control the “hype machine,” and how this “hype machine” has transformed society. ![]()
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