Magdalayna Curry | Searcharxiv

arXiv2026

The Body as Status: Muscularity, Engagement, and Body Image Risk on #GymTok

Body image concerns among boys and young men are increasingly oriented toward muscularity, with social media serving as a central context for communicating and evaluating these ideals. While prior research has focused on the thin-ideal, less is known about how the muscular-ideal is represented and reinforced on visual social media platforms. This study examines (1) dominant content themes, (2) perceived harm to body image, and (3) engagement patterns across #GymTok, a muscularity-oriented fitness subculture on TikTok. We conducted a content analysis of 2,210 #GymTok videos annotated by clinical experts across themes like self-objectification, rigid dieting, excessive exercise, supplement and steroid use, and masculinity. Annotators also rated the perceived harm of videos to the viewers' body image, and depicted bodies were coded according to muscularity level. Perceived harm varied across content themes, with supplement- and steroid-related content rated as most harmful. Engagement was positively associated with both muscularity and perceived harm: videos depicting more muscular bodies and those rated as more harmful received greater views, likes, shares, and comments. Although less prevalent, masculinity-focused content generated the highest engagement. These findings suggest that TikTok may not only expose users to muscular ideals and potentially harmful behaviors, but also algorithmically amplify them. By increasing the visibility of highly muscular and harmful content, recommendation systems may intensify social comparison processes, while objectification elevates the muscular body into a marker of status, masculinity, and social worth. Together, these dynamics may contribute to body image risk among boys and young men.

cs.CY↗

arXiv2025

BigTokDetect: A Clinically-Informed Vision-Language Modeling Framework for Detecting Pro-Bigorexia Videos on TikTok

Social media platforms face escalating challenges in detecting harmful content that promotes muscle dysmorphic behaviors and cognitions (bigorexia). This content can evade moderation by camouflaging as legitimate fitness advice and disproportionately affects adolescent males. We address this challenge with BigTokDetect, a clinically informed framework for identifying pro-bigorexia content on TikTok. We introduce BigTok, the first expert-annotated multimodal benchmark dataset of over 2,200 TikTok videos labeled by clinical psychiatrists across five categories and eighteen fine-grained subcategories. Comprehensive evaluation of state-of-the-art vision-language models reveals that while commercial zero-shot models achieve the highest accuracy on broad primary categories, supervised fine-tuning enables smaller open-source models to perform better on fine-grained subcategory detection. Ablation studies show that multimodal fusion improves performance by 5 to 15 percent, with video features providing the most discriminative signals. These findings support a grounded moderation approach that automates detection of explicit harms while flagging ambiguous content for human review, and they establish a scalable framework for harm mitigation in emerging mental health domains.

cs.CV↗