UX Case Study • Mobile App • Artificial Intelligence
Trucan: Fact-checking and Claim-making AI Assistant
Trucan is a 10 week academic case study researching and designing a concept to combat the spread of misinformation on social media. Following Google’s material design, Trucan enables users to check sources, learn from unbiased recommendations, share pre-checked posts, and compare content to learn which is more trustworthy.
Objective
Research and design a product or service using Artificial Intelligence
Course
UX Design: Innovation Studio
Role
Interaction Design, Visual Design, Content Strategy, Data Synthesis & Analysis, Primary Research, Observational Study Facilitator.
Team
JT Smith • Emily Ip • Wyatt Michel • Charleen Firlus
Did you know that you eat
8 spiders a year in your sleep?
You’ve probably read or heard that statement sometime in your life, but it’s actually false! It’s also an example of proving how quickly misinformation can spread online.
Initial Research
Social media platforms can be a breeding ground for misinformation. All it takes is for someone to share an initial post, and the spreading of lies ensues. Another issue is that misinformation can come in many forms; whether it be a text post, image, video, or even a comment. If we cannot easily distinguish a truth from a lie, what can?
How can AI help reduce misinformation?
Bot/spam detection
Information that is produced repeatedly and autonomously is detected as bot. Since information created by bots is produced faster than any human would be able to do.
Reputation Calculating
Using predictive analytics backed by Machine Learning, a website’s reputation can be predicted through considering multiple features like domain name and Alexa web rank.
Stance Detection
AI retrieves documents that are relevant to the claim, detecting the stance of those documents with respect to the claim. It can determine whether a certain document agrees, disagrees, or takes no stance on a specific claim.
Competitive Analysis Chart
Primary Research
Survey Insights
Expert Interviews
User Interviews
Misinformation Test
We used eye-tracking software along with a brief collection of true and fabricated social media and news posts to gather primary data on how people initially respond when presented information.
Click the arrows below to see the test we presented our participants.
Misinformation Test Results
One interesting takeaway from our observations was that older adults would focus towards text, while younger adults would focus on the image/icon. Different age groups perceived information differently, which pushed us towards focusing on combatting textual misinformation.