Methods: We measured the linguistic style of text messages exchanged by AMSM during a 5-week period (N=132 AMSM). Dyads were matched on age, geographical location, and sexual experience, but not linguistic similarity. We automatically extracted linguistic features, such as emotional content, part of speech tags, and pronouns. A regression model was trained to predict engagement and satisfaction based on dyad’s linguistic alignment.
Results: Linguistic alignment was positively correlated with the number of messages exchanged in the peer support platform (p<0.002). We also found that texters who composed messages with higher linguistic alignment and higher emotional content used condoms during sex and had more HIV tests relative to those whose messages had less linguistic alignment and emotional content.
Conclusions: Computational linguistics methods are efficient, scalable, and non-obtrusive in monitoring the implementation of text-based interventions. It reduces the need to hire and train personnel dedicated to measuring key features of the mHealth interventions delivery and implementation. Further, because the analysis of linguistic style can reveal the linguistic inter-personal differences, it can be used to tailor the content and timing of text-messages, and to optimize the peer-to-peer matching, based on participants’ linguistic profile.