One of the biggest areas to watch, of course, will be generative AI, particularly how it changes social media, political campaigning, and the fight over election misinformation. This confluence of new tech and big elections is also happening while the social media industry is going through major changes, including shifts in moderation approaches, legal battles, cuts to trust and safety teams, and platform shake-ups. This is all poised to make the future of the fight against misinformation murky, to say the least. It's a topic my colleagues and I take very seriously and have covered extensively in the past. And recently in MIT Technology Review, former Google boss Eric Schmidt penned an op-ed that lays out what he calls "a paradigm shift for social media platforms": The role of Facebook and others has conditioned our understanding of social media as centralized, global "public town squares" with a never-ending stream of content and frictionless feedback.

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics in the College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.

With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans. "We would like to know what a machine is thinking when it makes decisions, and how and why it agrees with the humans that trained it," Boukouvalas said. "We don't want to block someone's social media account because the model makes a biased decision."