Algorithmic gatekeepers: The democratic consequences of news selection algorithms
Navn på bevillingshaver
Arjen van Dalen
Institution
University of Southern Denmark
Beløb
DKK 960,210
År
2019
Bevillingstype
Monograph Fellowships
Hvad?
The news which citizens see and hear is no longer solely determined by the mass media, but increasingly by algorithms, which act as gatekeepers and automatically select, sort and prioritize information presented to the audience. Concerns have been raised about the democratic consequences. Techno-pessimists fear that algorithmic gatekeeping creates ‘filter bubbles’ or ‘echo chambers’ and polarizes public debate. Still empirical evidence for this is inconclusive. The purpose of my project is to write a book which helps us understand the process and democratic consequences of algorithmic news gatekeeping. The book analyzes how algorithms affect the selection and spread of news on different social media platforms, including Facebook, YouTube and Google News.
Hvorfor?
Worldwide, people get more and more of their political information through algorithm-controlled online social media. This is especially true for younger generations. While the influence of algorithms on our news diet is growing, there is a lack of transparency about how they work, and which effect they have. This leads to a lot of discussion about the potential positive and negative consequences, which is often not grounded in empirical knowledge. By providing new research evidence about the role of algorithms as gatekeepers in Denmark, this book will help to base these discussions on empirical knowledge. The book will furthermore develop new concepts and models which will help us understand and discuss this new phenomenon and its democratic consequences.
Hvordan?
Viral news stories on Facebook are analyzed to show how Facebook’s news feed algorithm affects the spread of public information. Video recommendations on YouTube are analyzed to assess whether YouTube’s recommendation algorithm exposes the audience to increasingly radical videos or to increasingly mainstream views. Analysis of personalized news recommendations by Google News reveals to which degree Google’s algorithm exposes the audience to diverse information. Methodologically, the case studies combine insight from experiments, observation studies and content analysis. Given the complexity of algorithmic gatekeeping and the lack of transparency around algorithms, only a mixed-methods design helps understand this new phenomenon.