Traditional approaches to evaluating citizens’ perceptions of democratic backsliding are based on theoretically driven concepts, such as responsive institutions, political rights, and civil liberties. They rely mainly on surveys and/or opinions polls, such as the World Values Survey, Afrobarometer and Eurobarometer. As noted by Helena Schwertheim (2017), these surveys often take scholarly assumptions as their starting point, and tailor the questionnaires depending on the focus of their research. Thus, scientific bias, is unavoidable – whether it would be in the definition of a certain concepts (‘what is democratic?’ where ‘democracy’ has an assigned value based on what political researchers deem to be democratic) or the answers (‘democracy is…’ – ‘peace’, ‘freedom’, ‘elections’). Contrary to the academic literature, this communication adopts an inductive method in order to investigate how citizens evaluate the quality of democracy based on their own judgement and not on predefined concepts. The empirical analysis is based on an original data set composed of 7429 citizen enquiries submitted to Europe Direct Contact Centre (EDCC), by Spanish citizens between December 2019 and December 2020. The time frame covers the main responses of the Spanish government to the COVID-19 health crisis. Using text mining and natural language processing, we identify different democratic backsliding trends that are related to but also go beyond the COVID-19 pandemic management, such as restrictions of movement, unemployment payments, the judicial reform and lack of compliance with European laws.
