Stuttering representation on Arabic-language Instagram: Who speaks and what gains visibility

Social media platforms play an increasing role in how stuttering is represented in public discourse. In Arabic-speaking contexts, little is known about the stuttering-related content available online, including who produces it, what types of advice circulate, and how engagement differs across sources. This study examined Arabic-language stuttering discourse on Instagram, focusing on associations among source identity, advice type, and post-level engagement (likes and comments).

A structured content analysis was conducted on 500 Arabic-language Instagram posts related to stuttering collected over seven months. Posts were coded for source identity, advice type, and valence. Associations between source identity, advice type, and engagement (likes and comments) were modeled using logistic and negative binomial regression. Qualitative contextual coding was used to interpret recurrent narrative patterns underlying quantitative associations. Intercoder reliability was assessed using Krippendorff's alpha, and sensitivity analyses were conducted to evaluate the robustness of model estimates.

Non-clinical sources, including special educators and digital content creators, produced the majority of posts (77.4%). Advice type varied significantly by source identity, χ²(8, N = 500) = 102.81, p < .001, Cramér's V = .45, with several non-clinical sources showing substantially higher odds of posting non-evidence-based advice (e.g., AORs ≈ 4.9-7.2). Engagement also differed by source and advice type. Non-evidence-based posts were associated with higher rates of likes (RR = 1.46, p = .004) and comments (RR = 1.39, p = .027), with the highest engagement observed for non-evidence-based posts authored by digital creators. Intercoder reliability ranged from Krippendorff's α = .72-.88 across coded variables. Qualitative contextual coding identified recurring narrative patterns centered on self-acceptance, perseverance, and claims of fluency change, alongside content with varying alignment with clinical perspectives.

Arabic-language Instagram reflects a discourse environment in which stuttering-related information is produced largely by non-clinical sources and engagement does not consistently correspond to clinical expertise. Visibility appears influenced by content characteristics, structural and cultural contexts, and platform dynamics. These findings point to a hybrid credibility system in which professional and experiential perspectives intersect and highlight considerations for digital health communication about stuttering in Arabic-speaking contexts. They also contribute to broader discussions of how health-related information circulates within digital environments.