Harnessing Natural Language Processing to decode community perspective

Abstract Information: In the face of the significant challenges posed by conflict-affected areas, the Save the Children activity in northern Mali, named Albarka, is pioneering efforts to enhance food and nutrition security while increasing community resilience. Funded by USAID’s Bureau for Humanitarian Assistance, this five-year Resilience Food Security Activity (RFSA) aims to fortify local systems and encourage community involvement. At its heart, Albarka seeks to improve feeding practices among the most vulnerable groups, including infants, women, and adolescents, to soften the blow of food and nutrition security crises. Through the conduct of  focus group discussions, the program has sought community insights on promoting beneficial behaviors like dietary diversity and sanitation practices. However, the abundance of qualitative data collected presented a unique challenge: ensuring the integrity of data analysis amidst potential biases stemming from the analysts' subjective perspectives. In response, Albarka has innovatively combined traditional paper-based data collection methods with advanced Natural Language Processing (NLP) technology, a subset of artificial intelligence. This hybrid approach aims to refine the analysis process, reducing biases and enhancing the quality of information gleaned from the discussions. Leveraging these insights, the Albarka Nutrition Team has validated and rolled out a series of small doable actions (SDAs), crafting a detailed workplan. This plan is designed to guide the introduction of new social behavior change (SBC) initiatives through local structures, marking a significant stride towards sustainable community empowerment and resilience in northern Mali.