Abstract
We describe a novel methodology combining social media listening (SML) and natural language processing (NLP) to examine women's involvement and the challenges they face in livestock and poultry production in Sub-Saharan Africa. Specifically, we explore women's roles, their perspectives and attitudes regarding disease prevention, treatment, vaccination and diagnostics in ruminant and poultry farming. Pulsar Platform™ was used to scrape social media data from X platform (Twitter), blogs, forums and Facebook using specifically designed Boolean search expressions. Large language models (LLMs) were used to filter and classify relevant posts. Audience profiling was conducted using Pulsar Platform™. Using a combination of a LLM and topic modelling we identified key themes in the data, which were subsequently summarised into narratives by a LLM. The findings were organised into four major themes regarding women’s roles in livestock and farming, challenges for women farmers, perspectives on disease management and control, and the value of training, education and animal health interventions for women and households in the region. Overall, this methodology demonstrated how SML data and NLP methods can successfully filter and analyse data from social media to provide pertinent real-world insights.