ABSTRACT
While user-generated online content (UGC) is increasingly available, public opinion studies are yet to fully exploit the abundance and richness of online data. This study contributes to the practical knowledge of user-generated online content and machine learning techniques that can be used for the analysis of UGC. For this purpose, we explore the potential of user-generated content and present an application of natural language pre-processing, text mining and sentiment analysis to the question of public satisfaction with healthcare systems. Concretely, we analyze 634 online comments reflecting attitudes towards healthcare services in different countries. Our analysis identifies the frequency of topics related to healthcare services in textual content of the comments and attempts to classify and rank national healthcare systems based on the respondents' sentiment scores. In this paper, we describe our approach, summarize our main findings, and compare them with the results from cross-national surveys. Finally, we outline the typical limitations inherent in the analysis of user-generated online content and suggest avenues for future research.
ABSTRACT
There is a growing recognition that trust attitudes towards national and European institutions are systematically related. Despite substantial research interest, the exact nature of this relationship remains unclear. We argue that a person-centered approach with its focus on identifying groups of individuals with similar attitudinal profiles provides a valuable complement to the currently standard variable-oriented approach in addressing this challenge. To illustrate, we examine the extent to which a prior typology of diffuse support for the European Union (EU) would emerge from a multilevel latent class analysis of the Eurobarometer data (2014). Our results provide support for the investigated typology by identifying four distinct trust profiles at the individual level that coalesce into separate latent clusters at the country level. Our findings underscore the value of a person-centered approach for the study of EU support.
ABSTRACT
The extensive literature on political trust has long suggested a link between macroeconomic conditions and public trust in political institutions. However, empirical evidence regarding this relationship remains ambiguous. Conflicting results appear to be related to differences in research design: while cross-sectional studies tend not to find evidence of a link between macroeconomic variables and trust in political institutions, most longitudinal studies do. In this paper, using recent advances in multilevel methodology, we examine both cross-sectional and longitudinal effects of macroeconomic variables on trust in national parliament within a single dynamic multilevel framework. By analyzing all seven waves of the European Social Survey (2002-2014), we demonstrate that declining macroeconomic performance has a negative within-country effect on trust in national parliament. At the same time, we find limited evidence in support of this association at the between-country level. This discrepancy suggests the presence of confounding factors that are unaccounted for in cross-sectional designs. We therefore argue for the importance of examining within-country effects as they provide a more stringent test of causality.
