A Natural Language Processing approach to measure expertise in the Delphi-based scenarios

The Delphi-based scenarios (DBS) development implies the assumption of different choices, through the gathering of information and the assessment of alternative resolutions (Panpatte and Takale, 2019). During the last decades, the spread of environmental hazards has increased quickly, much to request different responses in order to develop a sustainable future for humanity planning the present (McMichael and Lindgren, 2011). Since the DBS is a creative process (Nowack et al., 2011), different figures are selected to make choices, including academics, stakeholders and citizens. However, one of the main challenges remains the measurement of expertise, in fact, during the process, the experts should be assessed based on their competences in order to avoid any conflict in the final results and, eventually, weigh their answers. In recent years, some contributions adopted the self-assessments for the experts’ evaluation (Sossa et al., 2019), but many issues still remain (such as strong subjectivity and cognitive biases which produce over or underestimation). We develop a new method to estimate the expertise by using Natural Language Processing to acquire information, extracting the contributions of experts in each topic. First, starting from a draft list of selected experts, we identify the category of reference (e.g., academia, industry, local authority, citizens etc.). We build a data repository with the personal pages (URLs) of each expert to then use Python to extract from the URLs, the number of contributions related to a keyword, different for each category (e.g., publications for academics, reports and projects for stakeholders etc.). Finally, we proceed adopting a coefficient of production with a weighted sum of the results. To practically demonstrate our approach, we applied this method to a cohort of known experts, part of the “Smart control of the climate resilience” (SCORE) H2020 European project to estimate their expertise in specific areas.