How retrieval and ensemble methods make fact-checking faster, scalable, and more reliable in a digital world
In comparison to the traditional media, where articles are edited and verified before getting published, social media changed the approach completely. Suddenly, everyone could raise their voice. Posts are shared instantly, enabling the access to ideas and perspectives from all over the world. That was the dream, at least.
What began as an idea of protecting freedom of speech, giving individuals the opportunity to express opinions without censorship, has come with a trade-off. Very little information gets checked. And that makes it harder than ever to detect what’s accurate and what’s not.
An additional challenge is created as false claims rarely appear just once. They are often reshared on different platforms, often altered in wording, format, length, or even language, making detection and verification even more difficult. As these variations circulate across platforms they can seem familiar and therefore believable to its readers.
The original idea of a space for open, uncensored, and reliable information has run into a paradox. The very openness meant to empower people also makes it easy for misinformation to spread. That’s exactly where fact-checking systems come in.
Traditionally, fact-checking was a manual process that relied on experts (journalists, researchers, or fact-checking organizations) to verify claims by...
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