What can we do to reduce the spread of misinformation on social media? Evelyn Gosnell and Lindsay Juarez joined the Human Risk podcast to discuss our approach. On a recent project, we worked with social media platform TikTok, to reduce the spread of misinformation on the platform. By encouraging users to think before sharing certain types of content, we were able to reduce the spread of unverified information. In the episode, they explore how we went about this, what we learned from the experience and where we see the future of preventing the spread of misinformation. The conversation offers fascinating insights into a 21st-century human risk problem, that behavioral science can help to resolve.
Reducing the Spread of Misinformation on Social Media
Unsiloed: How to Leverage Behavioral Science to Market Your Mission and Raise More | With Evelyn Gosnell
When it comes to making consumer choices, it’s well known that humans don’t always act rationally. In fact, as amply documented by economists, we very often actually act against our own best interests! This episode of What the Fundraising is about the science of giving. Behavioral science, that is! If you haven’t been using the […]
How Behavioral Science Can Tackle Misinformation And Obesity
Small changes can have big effects. Standing on a different set of scales can affect our weight loss journey and adding friction to the share button online can reduce the spread of misinformation. Find out from Irrational Labs Managing Director Evelyn Gosnell how they are researching the behavioral insights that make a big difference in […]
Using Behavioral Science to Improve Your Product | Lenny’s Podcast
Irrational Labs CEO & Co-Founder Kristen Berman joins Lenny Rachitsky on Lenny’s Podcast to talk about how she helps companies like Google, Airbnb, PayPal, Microsoft, and LinkedIn improve their products and services through behavioral design research. Kristen shares Irrational Labs’ 3B Framework of Behavioral Design and illustrates what influences behavior change and the common biases […]