Promoting Diverse News Consumption Through User Control Mechanisms

News recommender systems (NRSs) are an essential component of online news portals. To avoid the emergence of “filter bubbles” where users display an overly selective perception of the news situation, NRSs must not only display a diverse range of news, but also motivate users to engage with the diversified content. Many existing approaches attempt to achieve this by modifying the recommendation strategy or by applying selection control techniques such as digital nudging. Based on insights from self- determination theory, we present an alternative approach that relies on user control mechanisms to promote self-determined motivation for exploratory use and thus diverse news consumption behavior. We also outline a methodological design to empirically confirm the viability of our approach. As such, we not only contribute to the theoretical understanding of the role of user control in diverse news consumption behavior, but also provide guidance on validating the practical feasibility of our approach

Unraveling Information-Limiting Environments: An Empirical Review of Individual, Social, and Technological Filters in Social Media

Social media platforms offer a convenient way for people to interact and exchange information. However, there are sustained concerns that filter bubbles and echo chambers create information-limiting environments (ILEs) for their users. Despite a well-developed conceptual understanding, the empirical evidence regarding the causes and supporting conditions of these ILEs remains inconclusive. This paper addresses this gap by applying the triple-filter-bubble model developed by Geschke et al. (2019) to analyze empirical literature on the individual, social, and technological causes of ILEs. While we identify some factors that increase the probability of ILEs under certain conditions, our findings do not suffice to thoroughly validate conceptual models that explain why ILEs emerge. Therefore, we call for future research to investigate the causes of ILEs with higher external validity to develop a more comprehensive understanding of this phenomenon

Understanding Information-Limiting Environments in Personalized News Platforms—A Systems Perspective

Personalized news platforms (PNPs) have become increasingly popular due to their ability to provide users with tailored, relevant news content. However, their algorithmic selection of news items bears the risk of limiting the diversity of news that users are exposed to, potentially creating information-limiting environments (ILEs). As the emergence of such ILEs involves interdependent sociotechnical interactions, classical positivist approaches have failed to establish an empirically verified theory on this phenomenon. Therefore, we adopt a systems perspective to conceptualize and quantify a model describing the sociotechnical interactions in PNPs that can create ILEs. We then propose an experimental approach to simulate these interactions with real users and validate its viability through an empirical prestudy. In doing so, we contribute to IS research by providing the conceptual and methodological basis for inductive theorizing on the causes of ILEs, which is an essential prerequisite for designing effective interventions to mitigate ILE-induced risks