Description:

Adolescent men who have sex with men (AMSM) experience a disproportionate burden of new HIV diagnoses among all young people. Fortunately, carefully designed mHealth interventions exist to reach and engage this key population. Mobile health (mHealth) is a general strategy to use mobile phones and other wireless mental health interventions. However, these interventions often send scripted messages while ignoring the linguistic style of participants or the linguistic context in which the scripted messages are received. Linguistic style and context affect people’s interpersonal satisfaction and engagement, as demonstrated in sociology, couple’s counseling, and psycholinguistics. For instance, married couples with similar linguistic styles report higher marital satisfaction and are less likely to separate. HIV research has largely ignored how mHealth participants’ linguistic style affects engagement and satisfaction to the intervention.

This lecture will describe computational linguistic methods that analyze the linguistic style of AMSM in order to optimize peer-to-peer platforms of HIV prevention programs. Also, these methods can inform ways to tailor scripted messages to the linguistic context of the peer-to-peer conversation in an efficient, scalable, non-obtrusive, and automatic manner. In summary, this lecture will demonstrate examples where computational linguistic methods could improve the implementation of future generation mHealth HIV interventions.

This lecture will be streamed on Blue Jeans for those who wish to join remotely. Meeting ID is 991 959 930.