Operationalizing brain rot on TikTok through linguistic, structural, and behavioral indicators of low-cognitive-value media content.

Abstract

Background and aims: This study investigates the linguistic, structural, and behavioral characteristics of "brain rot" content on TikTok, an emerging phenomenon defined by cognitively shallow, hyper-stimulating videos optimized for maximum passive engagement. The study aims to operationalize and detect "brain rot" content and to examine how such content relates to user engagement and interaction quality.

Methods: Drawing on a large-scale dataset of 78,452 TikTok videos, we employed a mixed-methods design combining Natural Language Processing, regression modeling, and qualitative coding. This approach enabled the identification of key textual, structural, and behavioral markers associated with low-cognitive-value media content.

Results: The findings show that videos under 15 seconds, clickbait language, high transition frequency, and emotionally triggering content are significantly associated with higher view counts but lower comment depth. The results also indicate that verified and unverified creators contribute to the diffusion of "brain rot" content across different topic areas, suggesting that platform architecture shapes both content visibility and content quality.

Discussion and conclusions: This study contributes a novel operational framework for identifying "brain rot" content and advances theoretical understanding of attention economics and algorithmic cultivation in short-form video environments. The findings highlight the broader implications of digital content strategies for user cognition, media literacy, and platform governance.