Detecting misinformation Detecting misinformation can be done by a combination of algorithms, machine-learning models, artificial intelligence, and humans.

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.

Spotting fake news Our research identifies linguistic characteristics to detect fake news using machine learning and natural language processing technology.

Currently, there are basically two types of tools to detect fake news. Firstly, there are automatic ones based on machine learning, of which (currently) only a few prototypes are in existence.

The freedom to share and access news online comes with the risk of falling into the trap of so-called fake news, and the development of neural networks and machine learning has only exacerbated the ...

So, a machine learning system such as this one is by no means a magic bullet to the problem of fake or biased news, but it certainly presents us with a valuable tool to help manage the ongoing issue.

But there’s hope that the use of deep learning can help automate some of the steps of the fake news detection pipeline and augment the capabilities of human fact-checkers.

Detecting fake news, at its source Date: October 5, 2018 Source: Massachusetts Institute of Technology, CSAIL Summary: A machine learning system aims to determine if a news outlet is accurate or ...

Bespoke fraud ML models are powered by algorithms that learn from historical data, picking up on behaviors and characteristics commonly associated with fraud.

Astroscreen is a startup that uses machine learning and disinformation analysts to detect social media manipulation. It has now secured $1 million in initial funding to progress its technology.