Toward Zeitgeist-Aware Multimodal (ZAM) Datasets of Pro-Eating Disorder Short-Form Videos.

Abstract

OBJECTIVE: Reliable identification of pro-eating disorder (pro-ED) content online suffers from two pervasive problems: (1) existing methods predominantly rely on text-based signals, failing to capture the inherently multimodal nature of multimedia content; and (2) these methods struggle to keep pace with the rapid evolution of references, memes, terminology, and contextual cues that underlie this content. Together, these limitations point to a gap: the absence of expert-annotated reference standards capable of supporting real-time research and robust multimodal detection model training for pro-ED content on short-form video (SFV) platforms. METHOD: To address this, we propose the development..