It’s no secret that fake news and disinformation are threatening our society. How is any discourse possible when we can’t agree on a baseline of truth?
With the scale of today’s (social) media, it’s impossible to keep track of everything manually. Clearly, the only way to solve fake news is through automation.
In the world of AI, fake news detection has been a hot topic for a few years now. While the technologies are advancing, there are still some critical challenges to overcome.
The context problem
Imagine looking at a tweet completely isolated, without any knowledge of the world.
Can you tell if it’s real? No. It’s just a bunch of phrases, words, letters.
Any single piece of information means nothing without context. That’s why we can’t simply train a machine learning model on a bunch of news labelled as real or fake.
One way to incorporate context is through stance detection. Here, we compare a claim, like a tweet, with another text. The model should predict the stance of the text towards the claim: Does it agree or disagree?