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How can we measure the impact – such as awareness for economic, ecological, and political matters – of information, such as scientific publications, user-generated content, and reports from the public administration, based on text data? This workshop brings together research from different theoretical paradigms and methodologies for the extraction of impact-relevant indicators from natural language text data and related meta-data. The papers in this workshop represent different types of expertise in different methods for analyzing text data; spanning the whole spectrum of qualitative, quantitative, and mixed methods techniques, as well as domain expertise in the field of impact measurement. The program was built to create an interdisciplinary half-day workshop where we discuss possibilities, limitations, and synergistic effects of different approaches.
The actual or anticipated impact of research projects can be documented in scientific publications and project reports. While project reports are available at varying level of accessibility, they might be rarely used or shared outside of academia. Moreover, a connection between outcomes of actual research project and potential secondary use might not be explicated in a project report. This paper outlines two methods for classifying and extracting the impact of publicly funded research projects. The first method is concerned with identifying impact categories and assigning these categories to research projects and their reports by extension by using subject matter experts; not considering the content of research reports. This process resulted in a classification schema that we describe in this paper. With the second method which is still work in progress, impact categories are extracted from the actual text data.