Detecting fake news, at its source
October 5, 2018
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 spending many millions
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 on the factuality of 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 postdoctoral associate Ramy Baly, 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."
Baly says the system needs only about 150 articles to reliably detect if
a news source can be trusted - meaning that an approach like theirs
could be used to help stamp out fake-news outlets before the stories
spread too widely.
The system is a collaboration between computer scientists at MIT CSAIL
and QCRI, which is part of the Hamad Bin Khalifa University in Qatar.
Researchers first took data from Media Bias/Fact Check (MBFC), a website
with human fact-checkers who analyze the accuracy and biases of more
than 2,000 news sites, from MSNBC and Fox News to low-traffic content
They then fed that data to a machine learning algorithm called a Support
Vector Machine (SVM) classifier, and programmed it to classify news
sites the same way as MBFC. When given a new news outlet, the system was
then 65 percent accurate at detecting whether it has a high, low or
medium level of "factuality," and roughly 70 percent accurate at
detecting if it is left-leaning, right-leaning or moderate.
The team determined that the most reliable ways to detect both fake news
and biased reporting were to look at the common linguistic features
across the source's stories, including sentiment, complexity and
For example, fake-news outlets were found to be more likely to use
language that is hyperbolic, subjective, and emotional. In terms of
bias, left-leaning outlets were more likely to have language that
related to concepts of harm/care and fairness/reciprocity, compared to
other qualities such as loyalty, authority and sanctity. (These
qualities represent the 5 "moral foundations," a popular theory in
Co-author Preslav Nakov says that the system also found correlations
with an outlet's Wikipedia page, which it assessed for general length -
longer is more credible - as well as target words like "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
"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.
"If outlets report differently on a particular topic, a site like
Politifact could instantly look at our 'fake news' scores for those
outlets to determine how much validity to give to different
perspectives," says Nakov, a senior scientist at QCRI.
Baly and Nakov co-wrote the new paper with MIT senior research scientist
James Glass alongside master's students Dimitar Alexandrov and Georgi
Karadzhov of Sofia University. The team will present the work later this
month at the 2018 Empirical Methods in Natural Language Processing (EMNLP)
conference in Brussels, Belgium.
The researchers also created a new open-source dataset of more than
1,000 news sources, annotated with factuality and bias scores - the
world's largest database of its kind. As next steps, the team will be
exploring whether the English-trained system can be adapted to other
languages, as well as to go beyond the traditional left/right bias to
explore region-specific biases (like the Muslim World's division between
religious and secular).
direction of research can shed light on what untrustworthy websites look
like and the kind of content they tend to share, which would be very
useful for both web designers and the wider public," says Andreas
Vlachos, a senior lecturer at the University of Cambridge who was not
involved in the project.
Nakov says that QCRI also has plans to roll out an app that helps users
step out of their political bubbles, responding to specific news items
by offering users a collection of articles that span the political
"It's interesting to think about new ways to present the news to
people," says Nakov. "Tools like this could help people give a bit more
thought to issues and explore other perspectives that they might not
have otherwise considered."