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In response to the growing HIV/AIDS and other health-related issues, UNICEFthrough their U-Report platform receives thousands of messages (SMS) every dayto provide prevention strategies, health case advice, and counsel- ing supportto vulnerable population. Due to a rapid increase in U-Report usage (up to 300%in last 3 years), plus approximately 1,000 new registrations each day, thevolume of messages has thus continued to increase, which made it impossible forthe team at UNICEF to process them in a timely manner. In this paper, wepresent a platform designed to perform automatic classification of shortmessages (SMS) in real-time to help UNICEF categorize and prioritizehealth-related messages as they arrive. We employ a hybrid approach, whichcombines human and machine intelligence that seeks to resolve the informationoverload issue by introducing processing of large-scale data at high-speedwhile maintaining a high classification accuracy. The system has recently beentested in conjunction with UNICEF in Zambia to classify short messages receivedvia the U-Report platform on various health related issues. The system isdesigned to enable UNICEF make sense of a large volume of short messages in atimely manner. In terms of evaluation, we report design choices, challenges,and performance of the system observed during the deployment to validate itseffectiveness.

Enabling Digital Health by Automatic Classification of Short Messages