Beyond Snapshots: A Multimodal User-Level Dataset for Depression Detection in Dynamic Social Media Streams

Abstract

As an increasing number of users share their lives and mental states on social media, many studies attempt to detect depression risk through social media videos using non-verbal cues like facial expressions, posture, gaze, and intonation from individual social media platforms, a proven effective field. However, these studies have focused on single-video level analysis to detect depression. These researches fail to capture the dynamic nature of social media streams and the complex, often gradual manifestation of depression. This limitation overlooks the comprehensive mental state of users, which can only be understood through their extended video histories. To address this, we introduce the Multimodal User-level Depression Detection Dataset (MUD3). MUD3 includes the long-term video histories of depressed users on social media platforms, containing user mental states across multiple videos and treating the video histories as a continuous social media stream. This allows us to model multiple videos at the user-level and analyze users' long-term mental states. MUD3 and supplementary materials are available at https://github.com/Syx1030/MUD3.