Industry updates

Fake news is a perennial problem but really begins to ramp up in the election season as conspiracy theories and misinformation by bad actors aim to manipulate voters. As the US election comes down to the wire in one of the closest races yet, Ben-Gurion University of the Negev researchers have developed a method to help fact-checkers keep up with the increasing volumes of misinformation on social media.

The team led by Dr. Nir Grinberg and Prof. Rami Puzis found that tracking fake news sources, rather than individual articles or posts, with their approach can significantly lower the burden on fact-checkers and produce reliable results over time.

"The problem today with the proliferation of fake news is that fact checkers are overwhelmed. They cannot fact-check everything, but the breadth of their coverage amidst a sea of social media content and user flags is unclear. Moreover, we know little about how successful fact-checkers are in getting to the most important content to fact-check. That prompted us to develop a machine learning approach that can help fact-checkers direct their attention better and boost their productivity," explains Dr. Grinberg.

Their findings were published recently as part of the Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Fake news sources tend to appear and disappear quite quickly over the years, so maintaining lists of sites is very cost and labor intensive. Their system considers the flow of information on social media and the audience's "appetite" for falsehoods, which locates more sites and is more robust over time.