ClipMind: A Framework for Auditing Short-Format Video Recommendations Using Multimodal AI Models

Abstract

We are witnessing a significant shift in social media platforms; we are transitioning from chronological social media feeds to feeds that are driven by AI recommendation systems. While the main goal of AI recommendation systems is to suggest engaging content to users, there are also some associated risks: AI recommendation systems can promote extreme content, causing negative consequences like online polarization and user radicalization. Overall, there is a pressing need to design powerful techniques that allow us to audit AI recommendation systems. Motivated by this, our work introduces ClipMind, a scalable and generalizable framework using advanced AI models to audit these recommendation algorithms on short-format video platforms like TikTok and YouTube Shorts. We demonstrate the merits of our framework by collecting social media feeds from TikTok. Our analysis shows that TikTok’s recommendation algorithm increasingly recommends similar videos when a user expresses interest in mainstream topics like Food and Beauty Care. On the other hand, by investigating niche interests (War and Mental Health), we find no evidence of informational rabbit holes of extreme content on TikTok. Our work contributes to efforts that leverage AI for social good, as our framework can be used by several interested stakeholders, including users, social media platforms, regulators, and researchers, to understand and audit video-based algorithmic recommendations.