Local communities in Uganda send UNICEF some 10,000 text messages (SMS) every week. These messages reflect the voices of Ugandan youths who use UNICEF’s U-report SMS platform to share their views on a range of social issues. Some messages are responses to polls created by UNICEF while others are unsolicited reports of problems that youths witness in their communities. About 40% of text messages received by UNICEF require an SMS reply providing advice or an answer to a question while 7% of messages require immediate action. Over 220,000 young people in Uganda have enrolled in U-report, with 200 to 1,000 new users joining on daily basis. UNICEF doesn’t have months or the staff to manually analyze this high volume and velocity of incoming text messages. This is where advanced computing comes in.
IBM recently partnered with UNICEF Uganda to develop an automated system to classify incoming text messages. (If this sounds familiar to iRevolution readers it is because my team and I at QCRI are developing a similar platform called Artificial Intelligence for Disaster Response, or AIDR. While our system is first and foremost geared towards classifying tweets, it can also be used to filter large volumes of SMS). The automated platform classifies incoming text messages into one (or more) of the following categories: water, health & nutrition, orphans & vulnerable children, violence against children, education, employment, social policy, emergency, u-report, energy, family & relationships, irrelevant and poll.
IBM created machine learning classifiers that are 40% more accurate than a keyword based approach for automate classification. The predictive quality of the individual classifiers ranged from a low of 69.8% for family & relationships and a high of 98.4 for water-related issues. See full list of results in table above. Note that the IBM platform is limited to English-based text messages but the team is looking to provide multi-lingual support in the future.
UNICEF is using this system to automatically route classified tweets to the appropriate departments. For example, UNICEF recently received a surge of text messages about nodding disease and responded by sending out a series of mass SMS’s to communities living in the affected region. These text messages provided information on how to recognize symptoms and ways to get treated. The feedback loop also includes government agencies and ministries. Indeed, all Members of Parliament and Chief Administrative Officers receive SMS updates based on the automated classification platform.
U-report is now being deployed in Zambia, South Sudan, Yemen, Democratic Republic of Congo, Zimbabwe and Burundi. I plan to get in touch with the team at IBM to learn more about these deployments and explore where we at QCRI may be able to help given our related work on AIDR. In the meantime, many thanks to my colleague Claudia Perlich for pointing me to this project. To learn more about IBM’s automated system, please see this paper (PDF).