Detecting fake news at its source
04 October 2018
This system needs only about 150 articles to detect the factuality of a news source — meaning it could be used to help stamp out new fake-news outlets before their stories spread too widely.

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MIT CSAIL

Lately the fact-checking world has been in a bit of a crisis. Sites like Politifact and Snopes have traditionally focused on specific claims, which is admirable but tedious; by the time they’ve gotten through verifying or debunking a fact, there’s a good chance it’s already traveled across the globe and back again.

Social media companies have also had mixed results limiting the spread of propaganda and misinformation. Facebook plans to have 20,000 human moderators by the end of the year, and is putting significant resources into developing its own fake-news-detecting algorithms.

Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) believe that the best approach is to focus not only on individual claims, but on the news sources themselves. Using this tack, they’ve demonstrated a new system that uses machine learning to determine if a source is accurate or politically biased.

Full story at MIT News: http://news.mit.edu/2018/mit-csail-machine-learning-system-detects-fake-...