LLM-based Automated Web Retrieval and Text Classification of Food Sharing Initiatives

Urban and peri-urban (UPU) food systems encounter challenges in sustainability and are fragile and vulnerable to shocks.Addressing these issues is one of the key drivers of food sharing initiatives (FSIs) which focus on collective acts around food across the food system.FSIs range from seed sharing and surplus food redistribution to community composting.We describe our development and deployment of web retrieval and content classification tools designed to provide automated mapping of FSIs at scale to populate databases of FSIs within cities.We present our novel automated system tailored for retrieving, identifying, categorizing and realtime monitoring of FSIs in over 200 European cities. Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency.The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks.Our system integrates web search using queries constructed automatically using domain-specific vocabulary resources with Large Language Model (LLM) query writing and classification methods.Experimental results using a collection of data derived from real online FSI content underscore the potential of digital automation to make significant contributions to innovative digital solutions to contemporary sustainability challenges.As such, the findings of this work pave the way for future research and implementation in similar contexts.