Since we cannot pay attention to all the posts in our feeds, algorithms determine what we see and what we don’t. The algorithms used by social media platforms today are designed to prioritize engaging posts – ones we’re likely to click on, react to and share. But a recent analysis found intentionally misleading pages got at least as much online sharing and reaction as real news.
This algorithmic bias toward engagement over truth reinforces our social and cognitive biases. As a result, when we follow links shared on social media, we tend to visit a smaller, more homogeneous set of sources than when we conduct a search and visit the top results.
Existing research shows that being in an echo chamber can make people more gullible about accepting unverified rumors. But we need to know a lot more about how different people respond to a single hoax: Some share it right away, others fact-check it first.
We are simulating a social network to study this competition between sharing and fact-checking. We are hoping to help untangle conflicting evidence about when fact-checking helps stop hoaxes from spreading and when it doesn’t. Our preliminary results suggest that the more segregated the community of hoax believers, the longer the hoax survives. Again, it’s not just about the hoax itself but also about the network.
Many people are trying to figure out what to do about all this. According to Mark Zuckerberg’s latest announcement, Facebook teams are testing potential options. And a group of college students has proposed a way to simply label shared links as “verified” or not.
Some solutions remain out of reach, at least for the moment. For example, we can’t yet teach artificial intelligence systems how to discern between truth and falsehood. But we can tell ranking algorithms to give higher priority to more reliable sources…..
We can make our fight against fake news more efficient if we better understand how bad information spreads. If, for example, bots are responsible for many of the falsehoods, we can focus attention on detecting them. If, alternatively, the problem is with echo chambers, perhaps we could design recommendation systems that don’t exclude differing views….(More)”