Social Networks, Truth and Trust

Originally posted in https://ideafair.bearblog.dev/social-network-and-trust/

One of the main issues with the current social networks is the issue of favoring appeal over truth. Posts that appear good get likes, regardless of whether they are actually true or not. Thus they are susceptible to people's biases. This leads to the problem of false news travelling faster than true news. Our current recommender systems promote posts that get a higher number of likes from people and thus this gets amplified. Instead we need to change our recommender systems to value truth over likings. Perhaps we can associate each user with a truth score based on how true their past predictions were and then promote posts from truthful people. This is similar to the ideas in the book "Signal and Noise" by Nate Silver. We can have a system where people can make testable predictions, something like https://longbets.org/ and develop a truth score for each user. Then the social network should promote posts of people who have high truth scores, rather than vocal bullshiters. Because many times truth is uncomfortable for us, but ultimately good in the long run. This is what leads to science.