Analyzing the Freedom of Information Requests Using Topic Modeling: Towards User-driven Open Government Data Ecosystems

Open data has become an important player in promoting government transparency and accountability around the globe. Governments have established policies to provide citizens or users with access to data. One way to obtain such data is through Freedom of Information Act (FOIA) requests. Considering users' requests for data and responding with the required information makes open data ecosystems user-driven. This paper explores freedom of information (FOI) requests received by Germany's Ministry of Health as a user-driven approach to open government data (OGD). By applying advanced natural language processing (NLP) techniques, specifically Latent Dirichlet Allocation (LDA) and BERTopic, we analyze a large corpus of FOI requests to identify key topics and trends in public inquiries. The findings reveal valuable insights into citizens' information demands and demonstrate the strengths of these NLP methods in extracting actionable patterns. In this way, governments can easily understand user needs in health-related datasets (this study, in particular, focuses on requests made by citizens related to COVID-19 data). This study lays the groundwork in two ways: first, by understanding and thematically categorizing user needs related to open data, and second, by improving government data transparency, informing the prioritization of dataset releases, and supporting evidence-based policymaking.