A tool developed by Keele University researchers has been shown to help detect fake news with an impressive 99% level of accuracy, offering a vital resource in combatting online misinformation. 

The researchers Dr Uchenna Ani, Dr Sangeeta Sangeeta, and Dr Patricia Asowo-Ayobode from Keele’s School of Computer Science and Mathematics, used a number of different machine learning techniques to develop their model, which can scan news content to give a judgement of whether a news source is trustworthy and genuine or not. 

The method developed by the researchers uses an “ensemble voting” technique, which combines the predictions of multiple different machine learning models to give an overall score. 

Impressively, this technique was accurate in identifying fake news 99% of the time, which significantly exceeded the researchers’ predictions and expectations. 

Their hope is that now the method can be further refined as AI and machine learning systems become more sophisticated, enabling them to eventually produce a model that is 100% accurate at identifying fake news. 

One of the lead authors Dr Uchenna Ani, Lecturer in Cyber Security at Keele, said: “In our constantly evolving digital communication landscape, the widespread dissemination of false information is a significant concern. It compromises the integrity of public discourse and has the potential to threaten both local and national security via influencing biased mindsets, views, and actions.  

“The risk posed by misinformation, disinformation, or fake news to the credibility of online news platforms, particularly on social media, highlights the urgent need for innovative solutions. We aim to enhance the capabilities of our AI solution through further research to help nip this problem in the bud.” 

The researchers recently presented their findings at the forty-fourth SGAI International Conference on Artificial Intelligence in Cambridge, United Kingdom.