Filter by author: Anne Reif

This special issue examines public (dis)trust in science amidst evolving digital media environments, marked by the increasing prevalence of online information sources about scientific topics such as climate change and COVID-19. This editorial summarizes the nine publications that are part of the special issue and shows how they address different aspects of public (dis)trust in science in the context of digital media environments. Furthermore, we reflect on the selection and production process and give an outlook as to where future research could be heading. The papers highlight various perspectives on (dis)trust in science in digital media environments to foster a deeper understanding of the role of digital communication.

A potential decline in public trust in science has often been linked to digital media environments, which serve as intermediaries of trust by providing cues for why (not) to trust science. This study examines whether exposure to trust cues in content affects public trust in science (across population groups). The study employs a mixed-method design, combining content analysis (“n” = 906) and panel survey data (“n” = 1,030) in Germany. The findings reveal that exposure to trust cues in certain media predicts public trust in science. Variations across trust groups indicate a nuanced nature of trust-assessing processes in digital media environments.

The measurement and analysis of people's knowledge on scientific topics, such as climate change, is challenging for researchers. One reason is that objectives are multi-dimensional and that probability is inherent. Moreover, uncertainties can exist on the individual's level among the public, but are rarely grasped by existing scales. Therefore, researchers must thoroughly consider what to measure and how. This paper theorizes five different dimensions of climate change knowledge. Three response scales including different degrees of confidence are applied on data from a German online survey (n=935); empirical results of multivariate regression analyses on attitudes are compared. Results highlight the importance of distinctively measuring dimensions and types of knowledge.