@inproceedings{chu-etal-2026-bigtokdetect,
    title = "{B}ig{T}ok{D}etect: A Clinically-Informed Vision{--}Language Modeling Framework for Detecting Pro-Bigorexia Videos on {T}ik{T}ok",
    author = "Chu, Minh Duc  and
      Pawar, Kshitij  and
      He, Zihao  and
      Sharifi, Roxanna  and
      Sonnenblick, Ross M.  and
      Curry, Magdalayna  and
      DAdamo, Laura  and
      Young, Lindsay  and
      Murray, Stuart  and
      Lerman, Kristina",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.33/",
    pages = "766--790",
    ISBN = "979-8-89176-380-7",
    abstract = "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."
}