Enhancing text analytics and capacity building of youth at risk
Challenge
UNHCR El Salvador collects semi-structured qualitative data from internally displaced people and community members at risk, which is analysed manually by the operation and partners. A more systematic analytical approach could improve the identification of overarching trends and patterns, which could strengthen existing advocacy efforts and community-based protection interventions.
Solution
Develop an analytical framework to support qualitative text data analysis that would provide additional insights. The project also involves community-based participatory aspects, with opportunities for at-risk youth in El Salvador to learn important data science skills.
Impact
Better insight into overarching protection trends and drivers of protection risks, their effects on communities, and possible community-based solutions. This will facilitate advocacy efforts, support programmatic planning, and help shape effective solutions.
Project impact
Other information
UNHCR El Salvador transformed the way participatory assessment data is analyzed by combining artificial intelligence, speech-to-text technology, and large language models to process one of the organization's largest qualitative datasets. The project converted more than 400 hours of focus group discussions involving 1,336 participants into structured, searchable evidence, allowing teams to rapidly identify protection concerns, barriers to education, livelihood challenges, and community priorities that would otherwise remain buried in thousands of pages of transcripts. Beyond improving decision-making, the initiative developed reusable tools for transcription, anonymization, and qualitative analysis that can be replicated across UNHCR operations. The project also invested in local talent, training youth at risk in data science and R programming, with 19 participants successfully completing the programme and contributing to the analysis of national survey and community data. The resulting approach demonstrated how generative AI can help humanitarian organizations better understand community voices at scale while maintaining data protection standards and reducing the time required for qualitative analysis. Read more about this initiative in our webstory.