Paper Type
ERF
Abstract
Mental health advocacy is becoming widespread and influential, especially in the era of excessive use of social media platforms. Although many studies examine the involved issues, few take a multidimensional view linking the expertise–authenticity tension with engagement metrics. This paper examines a central tension in Instagram mental health advocacy: credibility versus relatability and how this trade-off shapes user engagement. While prior research has studied influencer credibility, branded content, and mental health messaging, it has rarely systematically compared licensed vs. unlicensed advocates alongside branded vs. non-branded framing using large-scale, post-level evidence. To address this gap, we analyze posts from 41 verified Instagram accounts (May 2025–May 2026). Using Natural Language Processing (NLP) and Top2Vec topic modeling, we identify dominant mental health themes and examine associations with engagement outcomes (likes, comments, and views). This study offers a comparative, data-driven account of how credibility and relatability operate as parallel pathways to engagement.
Paper Number
1511
Recommended Citation
Yazdizadeh, Honeyeh and Farivar, Samira, "Who Should Talk About Mental Health on Instagram? Credibility Signals and Engagement Dynamics" (2026). AMCIS 2026 Proceedings. 20.
https://aisel.aisnet.org/amcis2026/conftheme/conftheme/20
Who Should Talk About Mental Health on Instagram? Credibility Signals and Engagement Dynamics
Mental health advocacy is becoming widespread and influential, especially in the era of excessive use of social media platforms. Although many studies examine the involved issues, few take a multidimensional view linking the expertise–authenticity tension with engagement metrics. This paper examines a central tension in Instagram mental health advocacy: credibility versus relatability and how this trade-off shapes user engagement. While prior research has studied influencer credibility, branded content, and mental health messaging, it has rarely systematically compared licensed vs. unlicensed advocates alongside branded vs. non-branded framing using large-scale, post-level evidence. To address this gap, we analyze posts from 41 verified Instagram accounts (May 2025–May 2026). Using Natural Language Processing (NLP) and Top2Vec topic modeling, we identify dominant mental health themes and examine associations with engagement outcomes (likes, comments, and views). This study offers a comparative, data-driven account of how credibility and relatability operate as parallel pathways to engagement.
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