Sinan Aral and his team discovered an interesting fact about fake news: it travels much faster than real news. For example, an article about Barack Obama possibly being injured was shared many more times than real news about this former president. This is because fake news is always much more surprising than real news, and people feel that they have been given a secret, urging them to send it to someone else. Aral gives several solutions to this. However, they all have their bad effects.
I believe one of the most important ones is labeling types of media. For example, much of the food we eat is labeled with details, but when it comes to the information we take in, not much is. When information is specified, it can tell us if the source is credible or where the source came from. Knowing this data, people can detect if something is real or false more easily. On the other hand, one big question is: who gets to decide what the labels are? Nevertheless, I believe that we should at least try this technique when trying to solve this issue.
Another solution to this problem is to use technology to figure out which news is fake, and which is not. Artificial Intelligence has a good reputation, such as Alexa from Amazon or Google Home. Using machine learning could slow down how fast these news are being spread much better than us humans could. Although this solution may seem almost faultless, there is one huge mistake in this. Machines do not know the ethics and philosophy of what is truth and what are lies.
I think that we, as humans, should try harder to find more solutions to implement in order to prevent fake news, which can create national disruption, such as the false news about President Obama being injured.