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From vaccine hesitancy to foreign interference and animal health, information manipulation in the digital space is on the rise, and the University of Ä¢¹½´«Ã½ is on the front lines of combatting it. 

To gauge just how much information manipulation is happening online, Dr. Jean-Christophe Boucher, PhD, and a transdisciplinary research group are scraping social media and using AI and machine-learning tools to classify it. 

Boucher is an associate professor of political science and fellow in the in the . In 2025, he served as special advisor and deputy director of data science with ’s Rapid Response Mechanism, where he helped build and lead a data-science team focused on identifying and analyzing foreign information manipulation and interference. 

Boucher recently sat down with UToday to discuss information manipulation in the digital space. 

How would you define information manipulation in the digital space?

Information manipulation is not simply a technological problem. It is increasingly a challenge for democratic governance and national security. Foreign states, political movements and commercial actors all seek to shape how citizens understand political issues, elections, public-health measures and international crises.

There are two ways actors manipulate the information space. On the one hand, there’s content manipulation, which is the creation and spread of false or misleading information on any issue. There are all sorts of ways to create false or misleading information, including through AI-generated content and deepfakes. Secondly, there is the manipulation of the spread and the amplification of information. This is where you may be using genuine content, but you are inauthentically inflating its spread by using bot networks. Actors usually do both at the same time, or either one. 

What are some of the markers you look for when trying to identify information that has been manipulated?

There are many ways to identify it, and we’ve built a suite of techniques and tools to analyze it. When you’re dealing with large-scale information manipulation, data analytics must play a role. The team I work with is dedicated to building transdisciplinary projects and now includes software engineers, computer scientists, data scientists, political scientists, sociologists, veterinarians and public-health professionals. My experience leading the data-science team at Canada’s Rapid Response Mechanism reinforced the importance of combining technical expertise with subject-matter knowledge when identifying information-manipulation campaigns. We create AI classification models that analyze claims and classify them based on their truthfulness. 

One challenge is that information manipulation increasingly targets scientific and technical issues that affect everyday life. Whether the issue is vaccine safety, animal health, foreign interference or emerging technologies, addressing manipulation requires both advanced data analytics and the scientific expertise needed to evaluate complex claims. A computer scientist can help build a detection model, but understanding whether a claim is misleading often requires experts in public health, veterinary medicine, security studies or other specialized fields.

When it comes to fake amplification, actors use different methods, and we’ve measured them. We can see actors create fake accounts to share, reply to, or otherwise engage with content to game the algorithm. We see various actors use these techniques, so it’s very difficult for untrained eyes to get a sense of whether this is something people really want to see or if it’s someone putting their thumb on the scale. 

Are there moments in time when you’d expect to see more information manipulation spread?

The expectation now is that information-manipulation strategies will be deployed at highly politically charged events. In democracies, there’s a focus on events related to democratic processes, such as elections, referendums and discussions on specific issues. For example, we are currently looking into the ostrich culling that occurred in British Columbia, and we’ve scraped social media to measure how this event coalesced around specific issues in animal health, as well as around political systems and anti-government narratives. We also see people manipulate the information space to make money. People can profit from monetization on social media platforms, so we see people flood social networks with false or misleading content that is shared and engaged with online solely for the purpose of making money.

How can people be aware of this information manipulation as they scroll?

I think people would be very surprised by how much inauthentic content there is on social media. A significant share of the content and engagement people encounter online is either automated, co-ordinated or otherwise inauthentic. Now that AI systems have been democratized, even more people can create this fake content or amplification. We’re at a point where it’s difficult to tell the differences, and surveys show most Canadians have a hard time discerning AI-generated content from authentic content. The most important thing people can do is approach online content with healthy skepticism. The challenge today isn’t just figuring out whether something is true or false. It’s understanding why you’re seeing it in the first place and how algorithms, co-ordinated networks and AI systems influence what appears in your feed.