Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks

TL;DR

This paper presents a real-time NLP-based system for detecting claims in news articles and retrieving similar fact-checked claims to improve factchecking efficiency amidst rising misinformation.

Contribution

It introduces a novel method leveraging NLP to compare incoming claims with a corpus and retrieve similar fact-checked claims in real-time.

Findings

Enables live comparison of claims with existing factchecks

Reduces duplication of factchecking efforts

Improves efficiency of the factchecking process

Abstract

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.