In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV. Even though the language of communication for the group was predominantly English, we observe frequent use of Kiswahili, Sheng and code-mixing among the three languages. We present an analysis of language choice and its accommodation, different functions of code-mixing, and relationship between sentiment and code-mixing. To explore the effectiveness of off-the-shelf Language Technologies (LT) in such situations, we attempt to build a sentiment analyzer for this dataset. Our experiments demonstrate the challenges of developing LT and therefore effective interventions for such forums and languages. We provide recommendations for language resources that should be built to address these challenges.

Access to mobile phones in many low- and middle-income countries has increased exponentially over the last 20 years, providing an opportunity to connect patients with healthcare interventions through mobile phones (known as mobile health). A barrier to large-scale implementation of interactive mobile health interventions is the human effort needed to manage participant messages. In this study, we explore the use of natural language processing to improve healthcare workers’ management of messages from pregnant and postpartum women in Kenya. Using multilingual, low-resource language text messages from the Mobile solutions for Women and Children’s health (Mobile WACh NEO) study, we developed models to assess urgency of incoming messages. We evaluated models using a novel approach that focuses on clinical usefulness in either triaging or prioritizing messages. Our best-performing models did not reach the threshold for clinical usefulness we set, but have the potential to improve nurse workflow and responsiveness to urgent messages.