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Human-Machine Communication

Rethinking Communication, Technology, and Ourselves


Edited By Andrea L. Guzman

From virtual assistants to social robots, people are increasingly interacting with intelligent and highly communicative technologies throughout their daily lives. This shift from communicating with people to communicating with people and machines challenges how scholars have theorized and studied communication. Human-Machine Communication: Rethinking Communication, Technology, and Ourselves addresses this transition in how people communicate and who, or what, they communicate with and the implications of this evolution for communication research. Geared toward scholars interested in people’s interactions with technology, this book serves as an introduction to human-machine communication (HMC) as a specific area of study within communication (encompassing human-computer interaction, human-robot interaction, and human-agent interaction) and to the research possibilities of HMC. This collection includes papers presented as part of a scholarly conference on HMC, along with invited works from noted researchers. Topics include defining HMC, theoretical approaches to HMC, applications of HMC, and the larger implications of HMC for self and society. The research presented here focuses on people’s interactions with multiple technologies (artificial intelligence, algorithms, and robots) used within different contexts (home, workplace, education, journalism, and healthcare) from a variety of epistemological and methodological approaches (empirical, rhetorical, and critical/cultural). Overall, Human-Machine Communication provides readers with an understanding of HMC in a way that supports and promotes further scholarly inquiry in a growing area of communication research.

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8. My Algorithm: User Perceptions of Algorithmic Recommendations in Cultural Contexts (Terje Colbjørnsen)


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8. My Algorithm: User Perceptions of Algorithmic Recommendations in Cultural Contexts


In the culture industries, discovery and recommendation have traditionally been the tasks of critics and insiders, be they professional or amateur (Hesmondhalgh, 2007; Maguire & Matthews, 2014). Today, the recommendations provided by cultural critics, reviewers, store clerks and knowledgeable fans are increasingly supplemented, enhanced and occasionally supplanted by automated services. If you wonder what to read, view or listen to, digital platforms are ready at hand with suggestions seemingly tailored specifically for you. These recommendations are the work of what are typically referred to as algorithms. In contexts of cultural consumption, algorithms offer targeted suggestions and cultural guidance based on computations of input from large reservoirs of user data. Automated functions for discovery and recommendations are important features of all the major players in digital media and culture. Spotify, Netflix and Amazon, arguably among the dominant cultural distributors in the digital sphere, all make recommendations based on your and your network’s listening, viewing and reading habits and expressed preferences: Spotify’s personalized Discover Weekly playlist is compiled by sourcing listening patterns of individual users as well as preferences logged by other users. Netflix offers automated recommendations by giving prominent display to certain titles from its vast catalogue based on calculations of ratings and usage. Amazon provides recommendations of the “you might also like”-kind by analysing purchase patterns across its millions of users and product categories...

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