For an AI system detecting fake news, to be super accurate, the hits need to be really high (say 90%) and so the misses will be very low (say 10%), and the false alarms need to stay low (say 10% ...

When researchers working on developing a machine learning-based tool for detecting fake news realized there wasn’t enough data to train their algorithms, they did the only rational thing: They ...

Fake news articles contained significantly higher levels of negative sentiment, including emotions such as fear, anger, ...

The language gives it away: How an algorithm can help us detect fake news Researchers are developing an algorithm that can distinguish between real and fake news articles.

The researchers conclude by highlighting open research challenges in the area of fake news detection. In 2017, two-thirds of U.S. adults get news from social media, a 5 percent jump over 2016 ...

Our fake news detector is built based on linguistic characteristics extracted from a large body of news articles. It takes a piece of text and shows how similar it is to the fake news and real ...

Genuine news, on the other hand, contains a larger proportion of words related to work (business) and money (economy). This suggests that a stylistic approach combined with machine learning might be ...

Researchers found that integrating emotional features, particularly negative emotions, into machine learning models enhances the accuracy of fake news detection on social media platforms. This ...

Efforts to detect fake news are not as advanced as they would appear, given that the best practices so far rely on pattern detection that can itself be exploited by malicious actors, according to ...