Algorithms, Automation, and News

New Directions in the Study of Computation and Journalism

Neil Thurman, Seth C. Lewis, Jessica Kunert, Neil Thurman, Seth C. Lewis, Jessica Kunert

• Editore: Routledge
• Anno: 2021
• ISBN:
  9781000384390
• Edizione: 1
• Argomento: Computer Science
• Categoria: Data Mining

This book examines the growing importance of algorithms and automation—including emerging forms of artificial intelligence—in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored.

Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on 'reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner.

The chapters in this book were originally published as a special issue of Digital Journalism.

INTRODUCTION

Algorithms, Automation, and News

Neil Thurman

, Seth C. Lewis

and Jessica Kunert

ABSTRACT

This special issue examines the growing importance of algorithms and automation in the gathering, composition, and distribution of news. It connects a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these articles share some of the noble ambitions of the pioneering publications on ‘reporting algorithms’, such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems, to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematize computational journalism by, for example, pointing out some of the challenges inherent in applying AI to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner.

In recent times, algorithms and automation have become pervasive if not always fully understood facets of contemporary life. What we read and watch, how we meet people and develop relationships, and how decisions are made about jobs, loans, and insurance—these and many other features of the everyday are increasingly influenced by mathematical models and the data-driven systems behind them, each with varying degrees of opacity regarding how they operate, in whose interests, and with what implications. Algorithms and associated forms of computational automation can be defined technically or socially (Zamith 2019). Technical definitions, common in computer and information sciences, affirm that an algorithm follows a series of pre-designed steps or rules toward solving a problem (Latzer et al. 2016); social definitions, more common in communication and media studies, emphasize the human–machine dynamics, institutional arrangements, and environmental conditions (among other things) that give shape to algorithms as social, cultural, and material artefacts (e.g., Gillespie 2016; Napoli 2014). Despite their long history, algorithms and automation have never been so front-and-centre as shaping forces in public life (as described well in accounts such as Bucher 2018 and Diakopoulos 2019). Most strikingly, and perhaps controversially across many domains, the ubiquity of computing capabilities and automated technologies has resulted in human decision-making being augmented, and even partially replaced, by software (Broussard 2018). Such augmentation and substitution is already common, and even predominates in some industries, including through forms of “communicative AI,” or artificial intelligence applied to contexts of human communication (Guzman and Lewis 2019). This trend is likewise rapidly accelerating in news media, leading one observer to conclude, “Algorithms today influence, to some extent, nearly every aspect of journalism, from the initial stages of news production to the latter stages of news consumption” (Zamith 2019: 1).

What exactly does such influence look like, and how are scholars and practitioners to make sense of it? That question animates this special issue of Digital Journalism. We began working on this project more than two years ago under the premise that, although the journalism studies literature had made great strides in assessing the digitization of news in the 2000s and the emergence, in the 2010s, of data, code, and software as key organizing components of contemporary journalism (see, e.g., Anderson, 2013; Ausserhofer et al. 2017; Lewis and Westlund 2015a; Usher 2016; Weber and Kosterich 2018), there was yet an opportunity to more fully capture and conceptualize the particular influence of algorithms and automation in newswork. By the mid-2010s, it had become clear that fully automated and semi-automated forms of gathering, filtering, composing, and sharing news had assumed a greater place in a growing number of newsrooms (Diakopoulos 2019; Dörr 2016), opening the possibility that there were places where shifts in the norms, patterns, and routines of news production were happening and even that, at a more fundamental level, taken-for-granted ideas about who (or what) does journalism were being challenged (Lewis, Guzman, and Schmidt 2019; Primo and Zago 2015). Some algorithms, for example, were being used to filter enormous quantities of content published on social media platforms, picking out what was potentially newsworthy and alerting journalists to its existence (Thurman et al. 2016; Fletcher et al. 2017). Other algorithms, meanwhile, were being used to produce automated journalism—thousands of stories at scale—by transforming structured data on sports results and financial earnings reports into narrative news texts with little or no human intervention (Carlson 2015). Moreover, by that point, automated processes were being used to test new forms of packaging and distributing news content, enabling consumers to request more of what they like and less of what they don’t and also making decisions on consumers’ behalf based on their behavioural traits, social networks, and personal characteristics (e.g., Thurman et al. 2019). And, in a larger sense, it was becoming apparent that algorithms, as part of a decades-long “quantitative turn” in journalism (Coddington 2015), needed to be understood as assemblages of human and machine—as configurations of social actors and technological actants (Lewis and Westlund 2015b) that require a more thoroughgoing investigation around issues such as algorithmic accountability (Diakopoulos 2015), the ethics of algorithms (Ananny 2016; Dörr and Hollnbuchner 2017), algorithmically organized information enclaves (Bruns 2019; Haim et al. 2018), and the symbolic value of machine-oriented journalistic work (Lewis and Zamith 2017; see also Bucher 2017).

Altogether, these developments have raised important questions about where algorithms and automation figure in relation to the social roles of journalism as a longstanding facilitator of public knowledge. In that spirit, this special issue represents a selection of papers that were originally presented at the 2018 Algorithms, Automation, and News Conference.¹ The articles in this special issue represent about a third of that conference programme and are introduced in more detail below. We have grouped the articles into four themes: ‘Publics and public service’, ‘Personalization and politics’, ‘Professionals and practices’, and ‘Promise and possibilities’.

Publics and Public Service

Although chatbots, a form of conversational user-interface (CUI), are familiar in other contexts, such as customer service, their use as a news distribution medium has been less common. However, this is starting to change, and the development and deployment of chatbots by two public-service news organizations, the BBC and the Australian Broadcasting Corporation (ABC), is the subject of two articles in this special issue. The adoption of chatbots has, in part, been driven by changes in the use of social media platforms, as people have moved away from more public channels, such as Facebook’s News Feed, to more private environments, such as WhatsApp and Facebook Messenger. Public service media (PSM), such as the BBC and ABC, often feel obligated to make their content, including news, available on the diverse media platforms that their audiences choose to use. Ford and Hutchinson’s (2019) special issue article is a case study of the ABC’s “newsbot,” and uses ethnographically inspired methods to examine how this chatbot mediates the relationships between the ABC and its audience. They find that some of the public who use the chatbot are broadly positive about it and appreciate the informal, colloquial mode of address and the control the bot gives them about what information they receive, where, and when. Some of the journalists behind the bot are also broadly positive, seeing it as a way to reach underserved audiences. Despite these positive outcomes, Ford and Hutchinson also address the implications and possible consequences that flow from the ABC chatbot’s reliance on the private infrastructure of Facebook and Chatfuel, including questions around who gets to own and use the public’s data.

Jones and Jones’ (2019a) special issue article is also a qualitative study of newsbots at a PSM organization, the BBC. As the authors show, the BBC has launched nearly a dozen newsbots across a mixture of third-party platforms—Twitter, Facebook, and Telegram—as well as on their own website, with some being conversational in nature. The article shows how, as with the ABC, the BBC’s experiments with bots have been in part prompted by a desire to reach and engage with underserved audiences, particularly the young. Jones and Jones make the important point that robust empirical evidence about the success of such strategies is still very limited. These two articles will, we hope, both inform and inspire further research in this area. The issues raised by the involvement of third parties in the development and hosting of PSM newsbots, as discussed by Ford and Hutchinson, were also apparent at the BBC, which has begun to develop strategies to ensure public service values are preserved.

Personalization and Politics

As both Ford and Hutchinson’s and Jones and Jones’ articles make clear, chatbots can make news appear more personal, both in its tone and content. The personalization of news content has a history stretching back decades (Thurman 2019a). It is, however, an ever-evolving phenomenon necessitating ongoing oversight from the research community. The special issue article by Bodó (2019) does just this through a qualitative study of algorithmic news personalization at twelve European “quality” news outlets. Automated news content personalization is often discussed in negative terms because of its supposed promotion of so-called filter bubbles and echo chambers (see Bruns 2019; Nechushtai and Lewis 2019), and is often treated as if it were a single, homogeneous phenomenon. Bodó’s article challenges this idea, making a crucial distinction between the personalization done by platforms and that done by publishers. For platforms such as Facebook, Bodó argues, personalization is driven by huge quantities of user data and content and enacted to maximize users’ engagement so that their attention can be sold to advertisers, all without much, if any, editorial oversight of the content recommendations made. He argues that, in contrast, the news publishers that are the focus of his study personalize content in a very different way, with different outcomes in mind. They are more hands-on, driven by a desire to sell subscriptions or demonstrate the benefits of public subsidies, which often means using personalization to try to cultivate interest in quality information, including hard news, and to promote journalistic authority and reliability.

The different ways in which news content personalization can be enacted are at the heart of Helberger’s (2019) special issue article, “On the Democratic Role of News Recommenders.” In it she contributes to ongoing debates about the perils and promise of personalization by developing a conceptual framework based on what she sees as the three main democratic theories used in academic work on the media. Firstly, the liberal tradition, in which individuals’ autonomy and rights to free expression and privacy are emphasized along with the decentralization of power. Secondly, the participatory model, which emphasizes a shared civic culture through the active participation of citizens. Thirdly, the deliberative theory of democracy, which shares much with the participatory model but has a greater focus on deliberation, with the media playing an important role as a sphere—open to all—in which many ideas are presented and debated.

Helberger then uses this democratic framework to examine the various roles that news recommenders have played, and may play, within society. She argues that the first wave of recommenders, including those on social media platforms, are broadly liberal in the priority they give to users’ interests, although rather illiberal in their market concentration and lack of transparency, and in how they collect and share users’ data. News recommenders that promote participatory democracy would, she suggests, put less priority on serving individual users’ tastes and more on providing information that reflects the interests of society more broadly and on seeking to encourage citizens’ involvement. Deliberative news recommenders are yet another step removed from liberal recommenders, placing the greatest level of importance on exposing users to a diversity of views and information and promoting discourse. With her article, Helberger casts fresh light on debates about news personalization, showing how judgements about its effects are very much dependent on one’s democratic values and how, like most technologies, recommender systems are neither inherently good nor bad. Their outcomes for democracy are very much dependent on the values with which they are imbued.

Stray’s (2019) article for this special issue also has the democratic role of the media at its heart but takes us to the other end of the news cycle, focusing on investigative news gathering rather than news distribution. He is interested in journalism’s watchdog role—how it can reveal wrongdoing and discourage corruption—and the part that technology, and AI in particular, might play in that. While Helberger’s article took political theory as its starting point, Stray’s is grounded in his understanding of how AI works, his reflections on the nature of the data to which journalists have access, and the legal and commercial context in which news is published. Stray shows how, despite hopes that computational journalism would enhance journalism’s watchdog role, the uses of AI in investigative journalism have, thus far, been modest. He suggests the reasons for this include the difficulties involved in acquiring data, the journalistic requirement for accuracy, the costs involved, the limitations of current technology, and the challenges involved in trying to codify news values. His article is a salutary lesson in how hopes that computation could be transformative for the way in which journalism is practiced have bumped up against the messy reality of the world as we find it. Nevertheless, Stray does see some near-term opportunities for AI in investigative journalism, particularly in the extraction of data from document caches and in how databases can be fused to reveal relationships that might otherwise remain hidden—for example, between offshore companies and their beneficial owners.

Professionals and Practices

An emphasis on news professionals and their practices has long been a central element of journalism studies in general and digital journalism studies in particular (e.g., see Eldridge et al. 2019; Robinson, Lewis, and Carlson 2019). This special issue is no exception. Following this tradition, Milosavljević and Vobič (2019) offer a comparative study of editors ...