How to study media diversity has become a major concern in today's media landscape. Many expect that algorithmic filtering and a shift of audiences from legacy media to new intermediaries decrease the diversity of news diets, leading to fragmented societies, polarization and spread of misinformation. Different fields, from journalism research to law and computer science, are involved in the study of media diversity. They operate, however, with vastly different vocabularies, frameworks, and measurements. To overcome this fragmentation, this study provides an extensive overview of conceptualizations and operationalizations of media diversity in different fields using a systematic literature review (1999-2018). This showed a lack of theorizing and linking of conceptual with empirical work in media diversity research. Based on this, we develop a framework on how to move forward: Regarding conceptualization, we call for focusing on different places in the journalistic information chain instead of the classical exposure-supply distinction. Methodologically, automated approaches (e.g., analyzing digital traces) and qualitative approaches (e.g., capturing perceptions of diversity) should receive more attention. For analysis, matters of balance and disparity need to be stressed more, especially discussing possible limits to diversity. Overall, research into media diversity thus needs to be addressed in interdisciplinary collaboration.
Reading news with a purpose Explaining user profiles for self-actualization
The digital traces that people leave through their use of various online platforms provide tremendous opportunities for studying human behavior. However, the collection of these data is hampered by legal, ethical, and technical challenges. We present a framework and tool for collecting these data through a data donation platform where consenting participants can securely submit their digital traces. This approach leverages recent developments in data rights that have given people more control over their own data, such as legislation that now mandates companies to make digital trace data available on request in a machine-readable format. By transparently requesting access to specific parts of this data for clearly communicated academic purposes, the data ownership and privacy of participants is respected, and researchers are less dependent on commercial organizations that store this data in proprietary archives. In this paper we outline the general design principles, the current state of the tool, and future development goals.
The digital traces that people leave through their use of various online platforms provide tremendous opportunities for studying human behavior. However, the collection of these data is hampered by legal, ethical and technical challenges. We present a framework and tool for collecting these data through a data donation platform where consenting participants can securely submit their digital traces. This approach leverages recent developments in data rights that have given people more control over their own data, such as legislation that now mandates companies to make digital trace data available on request in a machine-readable format. By transparently requesting access to specific parts of this data for clearly communicated academic purposes, the data ownership and privacy of participants is respected and researchers are less dependent on commercial organizations that store this data in proprietary archives. In this paper we outline the general design principles, the current state of the tool, and future development goals.
Today’s online news environment is increasingly characterized by personalized news selections, relying on algorithmic solutions for extracting relevant articles and composing an individual’s news diet. Yet, the impact of such recommendation algorithms on how we consume and perceive news is still understudied. We therefore developed one of the first software solutions to conduct studies on effects of news recommender systems in a realistic setting. The web app of our framework (called 3bij3) displays real-time news articles selected by different mechanisms. 3bij3 can be used to conduct large-scale field experiments, in which participants’ use of the site can be tracked over extended periods of time. Compared to previous work, 3bij3 gives researchers control over the recommendation system under study and creates a realistic environment for the participants. It integrates web scraping, different methods to compare and classify news articles, different recommender systems, a web interface for participants, gamification elements, and a user survey to enrich the behavioural measures obtained.
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