A corpus-assisted multimodal discourse analysis of glucagon-like peptide-1 receptor agonist narratives on TikTok

Nicola Pelizzari 1 *
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1 Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, Università di Brescia, Brescia, ITALY
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 16, Issue 2, Article No: e202626. https://doi.org/10.30935/ojcmt/18491
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ABSTRACT

This study investigates how glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are discursively constructed on TikTok through a corpus-assisted multimodal discourse analysis of 100 English-language videos tagged #glp1, comprising 137,032 words of transcribed speech and 254 minutes of content. Integrating keyword and collocation analysis, stance evaluation, MIPVU-based metaphor identification, and systematic multimodal annotation, the study examines dominant linguistic patterns, evaluative strategies, and semiotic resources shaping GLP-1 RA narratives. Results suggest a predominantly weight-normative and biomedical framing, with positive stance appearing prevalent across both verbal and non-verbal modes. Metaphorical framing appeared to center on four recurrent domains: transformation, struggle/battle, journey/progress, and cravings as noise, potentially reinforcing pharmacological intervention as simultaneously medical and personal. Multimodal features largely appeared to align with evaluative stance across semiotic layers, though descriptive patterns tentatively suggest variation by creator type. The study points to how TikTok’s communicative affordances may shape public understandings of pharmaceutical intervention, potentially privileging emotionally resonant narratives over clinically balanced discourse. It contributes to multimodal communication research by offering indicative evidence of how platform-specific semiotic resources may collaboratively shape health-related meaning-making, with possible implications for applied linguistics and digital health communication.
Keywords: Multimodal discourse analysis, corpus-assisted discourse analysis, TikTok, GLP-1 RA

CITATION

Pelizzari, N. (2026). A corpus-assisted multimodal discourse analysis of glucagon-like peptide-1 receptor agonist narratives on TikTok. Online Journal of Communication and Media Technologies, 16(2), e202626. https://doi.org/10.30935/ojcmt/18491

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