The heart of the matter is the distinction between provoking a response and providing content people want. Social media algorithms — the rules their computers follow in deciding the content that you see — rely heavily on people’s behavior to make these decisions. In particular, they watch for content that people respond to or “engage” with by liking, commenting and sharing.
As a computer scientist who studies the ways large numbers of people interact using technology, I understand the logic of using the wisdom of the crowds in these algorithms. I also see substantial pitfalls in how the social media companies do so in practice.
I was keen to track Twitter to understand the “wisdom of the crowds”. Social media fuels engagement based on the “outrage”. It is not conducive to debate issues around science or medicine, because people often take philosophical standpoints, and it doesn’t take too long before the conversation gets politicised. Science, itself has become politicised.
The linked write up has many interesting nuggets:
The wisdom of the crowds fails because it is built on the false assumption that the crowd is made up of diverse, independent sources. There may be several reasons this is not the case.
Second, because many people’s friends are friends of each other, they influence each other. A famous experiment demonstrated that knowing what music your friends like affects your own stated preferences. Your social desire to conform distorts your independent judgment.
Third, popularity signals can be gamed. Over the years, search engines have developed sophisticated techniques to counter so-called “link farms” and other schemes to manipulate search algorithms. Social media platforms, on the other hand, are just beginning to learn about their own vulnerabilities.
If I have a contrarian view on the paper (or a social-media post), it can quickly degenerate into misunderstanding. I’d have to live with it. Wisdom of the crowds is an overblown concept too. 280 characters can’t explain nuances. Tweet storms loose the context. While there are specific ways that engagement can be dialled down (by automated systems), it will be detrimental to the platform itself. Advertisement rates are determined on the basis of engagement metrics (which open up another can of worms). Social “likes” are virtual metrics that have no real-life value.
I have witnessed increased engagement with my tweets after I started extensively retweeting Twitter threads from specific accounts, even though it is automated. It says a lot about the platform too. However, it will bring my actual tweets across the timelines of other connected users. It is a mess.