Algorithmic Audience in the Age of Artificial Intelligence
Tailored Communication, Information Cocoons, Algorithmic Literacy, and News Literacy
Summary
The aim of the book is multifaceted: (1) to describe the phenomenon of AI-based news recommendation; (2) to explore the user experience of consuming recommended news; (3) to analyze the effects that algorithmic news consumption has on the audiences; (4) to raise awareness of the impact of algorithmic news consumption; (5) to inform the public, technocrats, and policy makers of the effects of algorithmic news consumption; and (6) to guide debate on ethical decision-making and possible policy change. Through an empirical investigation process, this volume examines algorithmic news consumption from a user perspective and dissects the complex effects caused by such consumption.
This book is suitable to be a primary text for undergraduate-level courses relating to media literacy issues and graduate-level courses with a particular focus on audience analysis in the age of artificial intelligence. It can also serve as a supplemental text for core courses in media/communication studies, such as Introduction to Communication, Current Issues in Communication, Communication Theory, and Communication Ethics.
"This comprehensive work uses original research to both focus and expand our understanding about the ways that the growing consumption of algorithmic news will impact both the news media business and participatory democracy. It provides sharp new insights at a critical moment in the evolution of journalism."
—Ryan Thornburg, Associate Professor of Journalism, School of Journalism and Media, University of North Carolina at Chapel Hill
"Roselyn Du’s book is a roadmap to understanding how the audience of today’s news are grappling with tailored communication, information cocoons, algorithmic literacy, and news literacy. This book is timely, insightful, and methodologically rigorous. This is a must read for students and scholars interested in algorithms and journalism."
—Kerk F Kee, Associate Professor of Media & Communication, Texas Tech University
"Guided by key theoretical considerations, this timely text details comprehensive empirical investigation of the effects of algorithmic news recommendations on news appreciation, news literacy, and public agenda priorities. Findings suggest that algorithmic news consumption may not be as dangerous as presumed and warned. A significant contribution of this work is support for the theoretical development of a renewed conception of the active audience and the redefinition of agenda-setting. A compelling case is made for the importance of research on algorithms and artificial intelligence for understanding the future of journalism and civic society."
—Cynthia King, Professor of Communication, California State University, Fullerton
Excerpt
Table Of Contents
- Cover
- Advance Praise
- Title
- copyright
- About the author
- About the book
- This eBook can be cited
- Table of Contents
- Acknowledgments
- Chapter 1: Algorithmic News Audience
- Chapter 2: A Brief History of News Recommendation Systems, Key Terms, and Definitions
- Chapter 3: Methods of Inquiry
- Chapter 4: The Pilot Study: Survey
- Chapter 5: The Pilot Study: Experiment and Interviews
- Chapter 6: The National Survey
- Chapter 7: Demographic Matters
- Chapter 8: The Stories Told by News App Users
- Chapter 9: Google News vs. Apple News
- Chapter 10: Concluding Remarks
- Series Index
Acknowledgments
There it is. College Park, Suite 650. Thank you for the workspace that allowed me to complete most parts of this book project. For more than one year, I stayed there longer than anywhere else.
I am thankful to AEJMC and Peter Lang for selecting my book proposal to be included in the prestigious AEJMC-Peter Lang Scholarsourcing Series. I signed the book contract with Peter Lang while I was in Hong Kong carrying a regular 2-2 teaching load, not knowing that I would be having an intercontinental relocation right after that, moving myself into a 4-4 teaching load job in California. As if this was not challenging enough, soon came COVID-19 while I was in the first year with my new institution. Life and work were turned upside down inopportunely. This book would not have come about but for all the support I have received from organizations and individuals. A sabbatical leave from Hong Kong Baptist University in 2017 allowed me the time to refresh my mind and start musing about the concepts of algorithmic audience and news literacy. When this research reached its critical phase in 2021, AEJMC supported my national survey with a Senior Scholar Grant and California State University Fullerton granted me one course release. My pilot study benefited greatly from colleagues and students across disciplines at Fullerton. These are kindhearted people who I have never met in person but were willing to go out of their way to support my research. Thanks are due to Alfonso Agnew, Jon Bruschke, Julia Chan, Lisa Erwin-Davidson, Tony ←ix | x→Fellow, Zac Johnson, and Mia Sevier, among others, for their generosity with time to help distribute the questionnaire to their students who enrolled in their programs or classes. My deep gratitude also goes to all the reviewers of this manuscript for their comprehensive feedback and their painstaking and meticulous comments.
The bulk of the research and writing took place under the COVID pandemic, concurrent with my first years in the entirely new environment of Cal State Fullerton. It was too unsettled a period for intense concentration required for scholarly contemplation, but thanks to my family, friends, students, and colleagues for the constancy of faith, hope, courage, and perseverance, I knew I would eventually reach this point, right here, sharing my research with a broader audience.
Chapter 1
Algorithmic News Audience
“I don’t pretend we have all the answers, but the questions are certainly worth thinking about.”
– Sir Arthur C. Clarke, media futurist
Imagine the following scenario: You grab your smart phone to check for messages, and a pop-up notification tells you that “Perseverance rover makes ‘completely unexpected’ volcanic discovery on Mars.” You’re interested, as you have been following news about Mars and rovers, but are also left wondering how your mobile service provider knows that this is of interest to you. A few stories later and you suddenly notice an advertisement for your favorite footwear product embedded into the story. How do they know this is your brand? Now, you are a little perturbed.
This scenario is nonfictional; it is currently happening to every one of us. Algorithm-based news recommendation systems are personalizing information by analyzing user interactions with content across platforms. Take for example the revamped “Google News” app, which replaced “Google Play Newsstand” in May 2018. It now features the “For You” section as the first choice, providing a personalized list of news stories which the algorithm decides the user might be interested in. The app is designed to track user data and adapt to user reading interests as well as a dynamic news environment, improving over time.
←1 | 2→Algorithms are increasingly shaping our online and offline lives as we are constantly exposed to algorithm-based web search results, targeted advertisements, personalized social media content, and personalized recommendations for consumption. Netflix recommends movies. Pandora recommends music. Amazon recommends books and everything to us. Well, we are probably fine with personalized entertainment and lifestyle products. But news is something is different. News, with its core values, should be fair, objective, impartial, and free of personal opinion and bias. Journalism has an essential role in a democratic society. The basic idea of personalized news recommendation seems to be at odds with this tenet.
The “age of artificial intelligence” has seen a global proliferation of AI-powered, algorithm-based news applications that cater to individual preferences. Such technology advancement for customized news consumption is favorable to news audiences as it allows easy and efficient access to relevant news and information, mitigating the human inability to sift through an enormous online space of existing news stories to reach a point of interest. Around the world, technology entrepreneurs partner with news content producers to make use of algorithms to make news apps. Online news aggregators like Feedly and Flipboard, as well as traditional independent news media like The New York Times and CNN, have all developed news apps that have personalized “For You” recommendations based on algorithms since 2018. Some technology reviewers and scholars noticed that AI-based news platforms were in full bloom in both China and the U.S. in 2020. According to a survey by Apple Inc., its news aggregator app, Apple News, had 125 million active users in the second quarter of 2020, which marked an increase of 40 million from the first quarter of 2019 (Figure 1.1). Meanwhile, subscriptions to Apple News+, which is a paid service integrated into Apple News, are also on the rise and could amass 100 million paid subscribers by 2023, according to analysts. The popularity of these algorithmic news apps makes a strong case for studying ←2 | 3→how artificial intelligence is transforming information distribution and news consumption, and therein the relationship between media and their audiences, and the implications for new types of media effects. It is, perhaps, both timely and necessary to examine various aspects of the phenomenon.
Figure 1.1Apple News App Active Users
Details
- Pages
- XII, 164
- Publication Year
- 2023
- ISBN (PDF)
- 9781433173608
- ISBN (ePUB)
- 9781433173615
- ISBN (MOBI)
- 9781433173622
- ISBN (Hardcover)
- 9781433173585
- ISBN (Softcover)
- 9781433173592
- DOI
- 10.3726/b16102
- Language
- English
- Publication date
- 2023 (February)
- Keywords
- artificial intelligence algorithmic news information cocoons algorithmic literacy news literacy tailored communication survey interviews experiment news app personalization news consumption Algorithmic Audience in The Age of Artificial Intelligence Tailored Communication, Information Cocoons, Algorithmic Literacy, and News Literacy Roselyn Du
- Published
- New York, Berlin, Bruxelles, Lausanne, Oxford, 2023. XII, 164 pp., 33 b/w ill, 47 tables.
