Autonomous fact-checking systems use multi-stage pipelines to detect and prioritize checkable claims.

Systems like Claim Buster do not just search blindly. They break the task into stages. First, they scan text to identify claims that are actually falsifiable, ignoring mere opinions. Then, they generate targeted questions using natural language processing. These queries are sent to search engines and structured knowledge bases like Wolfram Alpha. By comparing the retrieved answers, the system assigns a veracity verdict. This automated pipeline helps human fact-checkers prioritize the most urgent or viral claims.