Digital Journalism

The recent developments in generative artificial intelligence (AI) to produce new content (e.g., ChatGPT and Dall-E) are quickly landing on the front pages of most major newspapers and media outlets. Temptingly, the world of AI emerging technologies offers journalists a plethora of topics and buzzwords—from machine learning and neural networks to algorithms—to enhance their headlines and capture and hold the attention of their audiences.

But as the hype produced by AI has gripped journalists and audiences alike, causing media workers to struggle with grasping the meaning of these concepts, the technical details of the latest software, and the potential societal effects, the overuse of the "AI" buzzword is hindering a clear comprehension of the underlying ideas and consequences of implementing some of these technologies in society (O’Neil, 2016).

We refer to this phenomenon as media hype, a term frequently referenced in both popular culture and academic discourse (Borgman, 2007) that nevertheless lacks a concrete, universally accepted academic definition. Vasterman (2005), for example, argues that media hype should not refer to a gap in the news coverage between what technology promises to do and what it can do. Instead, Vasterman argues, hype simply describes the stimulation, amplification, and magnification of an issue by a self-reinforcing media-generated news wave.

On the contrary, a new wave of literature, such as Cave and Dihal (2019), argues that it is imperative to study the gap between AI’s possibilities and realities because the “perceptions of AI’s possibilities, which may be quite detached from the reality of the technology, can influence how it is developed, deployed and regulated” (p. 74). The phenomenon of media hype, when enveloping AI discussions, transcends simple technological hyperbole. As underscored by Vinsel and Funk (2022), this type of narrative often gives rise to a scenario that deviates from facts, creating concerns about its potential to obscure critical underlying societal concerns.

Understanding how to define, measure, and navigate media hype in relation to AI is particularly important today because, from autonomous vehicles and robotaxis to chatbots and metaverses, how these emerging technologies are portrayed in the media —but also created and shaped by news organisations— can influence people’s attitudes and significantly influence policy decision-making worldwide (Brennen et al., 2020). In this context, we seek to understand how hypes are born, what reporting responsibly on hypes really means, how media intersects with other actors (such as big tech, policymakers, and influencers), and the effects and consequences of hype reporting for civil society.

This special issue of Digital Journalism aims to understand the intersection of media hype with how news organisations cover innovations in artificial intelligence. With growing scholarly attention to the phenomenon, it is essential to understand how journalists are reporting on fundamental technological advances, identifying the benefits that come from many of these new technologies, but at the same time avoiding the temptations to focus on the latest gadgets, apps, and trends rather than the broader AI’s societal implication. Indeed, this issue invites proposals describing how journalists and news organisations respond to AI hype in myriad strategic ways, such as through investigation and critical treatment of AI innovation and capability or of the companies pushing hyped claims. This issue also explores how journalists and newsrooms may fail to be critical of these issues. It also offers the opportunity for the development and extension of theory regarding the meaning-making functions of journalism, within the newsroom and with audiences, as well as the examination of the power dynamics at play regarding technology, media, and society.

We encourage submissions from diverse disciplines and contexts. We invite contributions of theoretical, methodological, and empirical manuscripts, including different philosophical and theoretical approaches and empirical methods (i.e., content analysis, interviewing, critical and cultural analysis, and critique) that want to move the field forward. Possible topics may include (but are not limited to):

* The media coverage of emerging technological innovation in AI, including its intersections with related areas of robotics,
immersive technologies, and others.
* Conceptual interventions and clarifications regarding the media industry’s AI coverage, hype in media innovation, and hi-tech news.
* Examination of professional practices and norms in relation to media coverage of AI and AI- related technology.
* Audience perspectives on media coverage of AI and AI-related technologies.
* Historical and cultural analysis of media hype surrounding AI and related technologies in contrast and comparison to previous technological innovation.
* Methodological and contextual challenges (skills and expertise) for critical tech news and algorithmic accountability reporting,
including issues of transparency and accountability in the tech industry.
* The mechanisms, economics, actors and ecosystems of hype, including connections to the role of social media platforms, algorithms, and other actors in contributing to media hype.
* New role performances for the coverage of AI and how “hype” interacts with other factors, such as costs and time savings, and/or more normative principles, such as investigative/critical journalism.
* The borderline between hypes and myths and the ethical questions about to what extent journalists have a role in debunking and critically scrutinising hypes.
* Methodological approaches to the study of media hype and innovations in artificial intelligence, including content analysis, interview studies, survey research, discourse analysis, and comparative analysis across different media platforms and countries.
* The dynamics and effects of hypes on public debates, policy process, and technology regulation.

The special issue is open for regular submissions; decisions about inclusion will be quality-based and reliant on thorough peer-review.

Please submit an abstract of approximately 500 to 750 words (not including references) in either an MS Word or PDF file format to [email protected] by January 10, 2024. Authors of accepted abstracts are expected to develop and submit their original article for the journal’s full peer-review process by the stated deadline. Articles should be between 7,000 and 9,000 words in length, following the journal’s style guidelines. No payment from the authors will be required.

Abstract submission deadline: January 10, 2024
Notification on submitted abstracts: February 16, 2024
Article submission deadline: June 15, 2024

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