At MIT: “Detecting Fake News at its Source: Machine Learning System Aims to Determine if an Information Outlet is Accurate or Biased”
From MIT News:
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.
“If a website has published fake news before, there’s a good chance they’ll do it again,” says postdoc Ramy Baly, the lead author on a new paper about the system. “By automatically scraping data about these sites, the hope is that our system can help figure out which ones are likely to do it in the first place.”
Co-author Preslav Nakov, a senior scientist at QCRI, says that the system also found correlations with an outlet’s Wikipedia page, which it assessed for general — longer is more credible — as well as target words such as “extreme” or “conspiracy theory.” It even found correlations with the text structure of a source’s URLs: Those that had lots of special characters and complicated subdirectories, for example, were associated with less reliable sources.
“Since it is much easier to obtain ground truth on sources [than on articles], this method is able to provide direct and accurate predictions regarding the type of content distributed by these sources,” says Sibel Adali, a professor of computer science at Rensselaer Polytechnic Institute who was not involved in the project.
Nakov is quick to caution that the system is still a work in progress, and that, even with improvements in accuracy, it would work best in conjunction with traditional fact-checkers.
Read the Complete Article
Research Article: Predicting Factuality of Reporting and Bias of News Media Sources
About Gary Price
Gary Price (email@example.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.