Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
This addresses the challenge of increasing false information and shrinking newsroom budgets for journalists and factcheckers, though it is incremental as it builds on existing NLP techniques.
The paper tackles the problem of inefficient factchecking in journalism by proposing an NLP-based method that compares incoming claims to a corpus of factchecked claims in real-time, enabling factcheckers to avoid duplicate work.
Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest developments in Natural Language Processing (NLP). This method allows us to compare incoming claims to an existing corpus and return similar, factchecked, claims in a live system-allowing factcheckers to work simultaneously without duplicating their work.