Modeling Misinformation

Computer scientist William Wang’s deep learning project aims to understand the spread of online misinformation
Monday, July 29, 2019 - 09:30
Santa Barbara, CA

William Wang.jpg

William Wang

William Wang

Photo Credit: 

Courtesy image


Problem is, the average online browser/social media user doesn’t have the time or wherewithal to investigate the legitimacy or provenance of everything that shows up on their feeds. And it is this vulnerability that less scrupulous content generators exploit to spread misinformation, with results that can range from a little egg-on-face embarrassment to downright life-changing or potentially deadly consequences.


“So the question is, given a post, how would you be able to understand whether this is specifically misleading or if this is a genuine post,” Wang said, “and, given the structure of the network, can you identify the spread of misinformation and how it is going to be different compared to standard or nonstandard articles?”

It’s a tall order, especially in the social media arena, which has leveled the playing field between legitimate, established news websites and questionable sites that do their best to look official, or appeal to a user’s emotions before they can step back and question the source of their information.

However, thanks to natural language processing — Wang’s specialty — the text in these posts and articles can be used to reveal information about their creators and propagators, such as their affiliations, ideologies and incentives for posting, as well as who their intended audience may be. The algorithm crawls through millions of news articles posted by anonymized users on platforms such as Twitter and Reddit and examines the articles’ titles, content and links. The purpose is to get a sense not only of the entities behind them, but also of their patterns of dissemination across the network.

“A lot of us take websites for granted and casually retweet or repost misinformation and that’s how it gets propagated, cascades and spreads virally,” Wang said. “Some of the most important questions we’re asking are: What are the patterns? What are the incentives?”



“Clickbait mainly is low-quality articles which can indeed contain a lot of misinformation and false information because they have to exaggerate,” Wang said. Together with computer science Ph.D. student Jiawei Wu, the team developed a method called “reinforced co-training,” which employs an efficient system of labeling a few hundred articles that are then used to train a machine learning classifier to label what it thinks may be clickbait in an enormous, million-story dataset.

“Then we take these newly labeled instances and retrain the classifier,” Wang said. “This iterative process allows us to collect more label data over time,” he added, which refines the accuracy of the tool.


“That’s really the beauty of natural language processing and machine learning,” Wang said. “We have a huge amount of data in different formats, and the question is: How do you turn unstructured data into structured knowledge? That’s one of the goals of deep learning and of data science.”

Contact Info: 

Sonia Fernandez
(805) 893-4765