JMIR Publications

ABSTRACT

Background:

HIV mHealth interventions often incorporate interactive peer-to-peer features. The user-generated content (UGC) created by these features can hold valuable design insights by revealing what topics and life events are most salient for participants, which can serve as targets for subsequent intervention. But unstructured, textual UGC can be difficult to analyze. Interpretive thematic analyses can preserve rich narratives and latent themes, but are labor-intensive and therefore scale poorly. Natural language processing (NLP) methods scale more readily, but often produce only coarse descriptive results. Recent calls to advance the field have emphasized the untapped potential of combined NLP and qualitative analyses toward advancing user attunement in next-generation mHealth.

Objective:

In this proof-of-concept analysis, we gain human-centered design insights by applying hybrid consecutive NLP-qualitative methods to UGC from an HIV mHealth forum.

Methods:

UGC was extracted from [MASKED], a webapp intervention for men living with HIV that includes an unstructured peer-to-peer support forum. In Python, topics were modelled by latent Dirichlet allocation. Rule-based sentiment analysis scored interactions by emotional valence. Using a novel ranking standard, the experientially richest and most emotionally polarized segments of UGC were condensed, then analyzed thematically in Dedoose. Design insights were then distilled from these themes.

Results:

The refined topic model detected K = 3 topics: A: Disease coping; B: Social adversities; C. Salutations, check-ins. Strong intra-topic themes included HIV medication adherence, survivorship, and relationship challenges. Negative UGC often concerned strong negative reactions to external media events. Positive UGC often focused on gratitude for survival, wellbeing, and fellow users’ support.

Conclusions:

With routinization, hybrid NLP-qualitative methods may be viable to rapidly characterize UGC in mHealth environments. Design principles point to opportunities to align mHealth intervention features with the organically occurring uses captured in these analyses: for example, by foregrounding inspiring personal narratives and expressions of gratitude, or de-emphasizing anger-inducing media.