(My photo – that’s my celebratory cake!)
… remember the “Algorithms, Automation, and News“ conference? Yes, the one I co-organised in May 2018 in Munich? Back then, we promised to publish a special issue in Digital Journalism with the best articles of the conference… and now we deliver!
The special issue features our editorial, written by Neil Thurman, Seth Lewis, and me, and, of course, the great articles we selected. Enjoy!
Our editoral tells you what the special issue is all about:
Algorithms, Automation, and News
Neil Thurman , Seth C. Lewis & Jessica Kunert
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.
And here are the fabulous articles:
On the Democratic Role of News Recommenders
Click here to see Natali’s abstract
Are algorithmic news recommenders a threat to the democratic role of the media? Or are they an opportunity, and, if so, how would news recommenders need to be designed to advance values and goals that we consider essential in a democratic society? These are central questions in the ongoing academic and policy debate about the likely implications of data analytics and machine learning for the democratic role of the media and the shift from traditional mass-media modes of distribution towards more personalised news and platforms Building on democratic theory and the growing body of literature about the digital turn in journalism, this article offers a conceptual framework for assessing the threats and opportunities around the democratic role of news recommenders, and develops a typology of different ‘democratic recommenders’.
Newsbots That Mediate Journalist and Audience Relationships
Heather Ford & Jonathon Hutchinson
Click here to see Heather’s and Jonathon’s abstract
News media organisations are experimenting with a new generation of newsbots that move beyond automated headline delivery to the delivery of news according to a conversational format within the context of private messaging services. To build the newsbot, journalists craft statements and answers to users’ questions that mimic a natural conversation between a journalist and user. In so doing, journalists are experimenting with styles of communication that reflect very particular journalistic personas. We investigate the persona of the news chatbot created by the Australian Broadcasting Corporation (ABC), the better to understand how the public broadcaster’s forays into social media service delivery and automation are shaping new relationships between public service broadcasters and their audiences. We find that, for a section of the audience that uses it, the friendly newsbot contrasts favourably with their previous experience with news and the journalists who produce it. The public service journalists who operate the bot are, in turn, using the bot to try to reach new audiences by experimenting with a more informal, intimate relationship with citizen users. The supposedly “intelligent” (but in actual fact very much human-crafted) newsbot is the vehicle through which this new relationship is being forged.
Public Service Chatbots: Automating Conversation with BBC News
Bronwyn Jones & Rhianne Jones
Click here to see Bronwyn’s and Rhianne’s abstract
Automation of journalistic tasks is growing with the development of increasingly sophisticated software for newsgathering, production, and distribution. Bots are one form of algorithmic technology that has found a place in the modern newsroom, with chatbots leading the way as news organisations seek to attract new audiences using conversational forms of journalism. Recent advances in artificial intelligence (AI) and machine learning (ML) have fuelled increasing experimentation with machine autonomy and there has been much hyperbole in the press about the extent and impact of this on journalism. Looking at on-the-ground trials in audience-facing bots at the UK’s largest public broadcaster, we find a significantly more restricted picture. News bots at The BBC to-date have been basic, do not use ML, and have rarely been integrated into news production. The organisation is laying groundwork for development of more interactive news formats with an increasingly conversational tone and individual mode of address as part of a strategy for increased personalisation, which is likely to involve growing levels of ML. In the process, bots are reconfiguring working practices and infrastructure, posing new editorial and technical challenges, and redefining relationships with audiences. We discuss the implications of this for public service media.
Click here to see Balázs‘ abstract
How do news organizations design and implement algorithmically personalized news services? We conducted 16 in-depth interviews with professionals working in European public service broadcasting and commercial quality news media to answer this question. The news business is undergoing rapid transformations regarding how news production is financed, how news is produced and delivered to audiences and how citizens consume news. In all of these changes algorithmic recommender systems play a role. We focus on news organizations’ own personalized news services, and analyze how they define the role of personalization in contributing to the financial success of the organization, in reaching and retaining audiences, and in fulfilling their editorial mission. We interviewed editors, journalists, technologists and business intelligence and publishing professionals to gain a structural understanding of the often conflicting goals of personalization. We found that rather than focusing on increasing short-term user engagement, European quality news media try to use news personalization to increase long-term audience loyalty. In distinction to the “platform logic of personalization”, which uses personalization to produce engagement and sell audiences to advertisers, they have developed a “news logic of personalization”, which uses personalization to sell news to audiences.
Click here to see Jonathan’s abstract
Many have envisioned the use of AI methods to find hidden patterns of public interest in large volumes of data, greatly reducing the cost of investigative journalism. But so far only a few investigative stories have utilized AI methods, in relatively narrow ways. This paper surveys what has been accomplished in investigative reporting using AI techniques, why it has been difficult to apply more advanced methods, and what sorts of investigative journalism problems might be solved by AI in the near term. Journalism problems are often unique to a particular story, which means that training data is not readily available and the cost of complex models cannot be amortized over multiple projects. Much of the data relevant to a story is not publicly accessible but in the hands of governments and private entities, often requiring collection, negotiation, or purchase. Journalistic inference requires very high accuracy, or extensive manual checking, to avoid the risk of libel. The factors that make some set of facts “newsworthy” are deeply sociopolitical and therefore difficult to encode computationally. The biggest near-term potential for AI in investigative journalism lies in data preparation tasks, such as data extraction from diverse documents and probabilistic cross-database record linkage.
Human Still in the Loop
Editors Reconsider the Ideals of Professional Journalism Through Automation
Marko Milosavljević & Igor Vobič
Click here to see Marko’s and Igor’s abstract
The study investigates how automation novelties in the newsroom both challenge and maintain the core values of journalism’s professional ideology. Building on semi-structured interviews with editors of legacy news institutions in the United Kingdom and Germany, the study reveals the rationales behind the changing journalism–technology relationship and the dynamics of the re-articulation of the core ideals of journalism. In discussing automation with respect to strategic newsroom development, the interviewees see journalism’s professional ideology as being in a state of flux. They identify contradictions between automation and some of journalism’s core ideals (public service, autonomy, and objectivity) and acknowledge both the potential and limits of technology with regard to others (timeliness and ethics). Despite the growing relevance of automation for news production, human journalists are still regarded as the dominant agents in news production and its continuous reinvention. This human-still-in-the-loop perspective highlights the idea that journalism is undergoing a profound yet long transformation where new technologies are not simply appearing and changing everything, but are innovations developed and embedded in established relations of the news production process. This perspective both reiterates and challenges the prevailing meanings of journalism.
Click here to see Matt’s abstract
This article interrogates the relationship between epistemic authority and journalistic technology through the perspective of mechanical objectivity—a belief in technological systems capable of rendering a particular output in a manner that overcomes the limits of human subjectivity. By treating journalistic objectivity not as a stable referent, but as a contextual one prone to shifts in practices and understandings over time, it foregrounds how changing technologies of recording, creating, and distributing news content affect how journalistic objectivity is understood. Following this perspective, two technological practices are examined: photojournalism and algorithms. The development of photojournalism led to the prizing of news images as objective representations produced by the camera to the diminishment of human judgment. Similarly, the various outputs produced by news algorithms are accompanied by an orientation toward computational objectivity in contrast to human subjectivity. Exploring these dynamics sheds light on the ongoing relationship between news technologies and discourses of journalistic objectivity in the face of digital innovations in the production and circulation of news.
Structured Journalism and the Semantic Units of News
Click here to see David’s abstract
The growing influence of computation on news has intensified the need for an analytical framework that describes the common foundations of different computational approaches to journalism. This article proposes such a framework, founded on the concept of units of journalistic knowledge smaller than the article, expressed partially or completely as structured data and positioned along a continuum of news artifacts. The emerging practice of structured journalism and its use of “atomized” news is described and is presented as an embodiment of the framework. Different approaches to the use of computation within journalism are then positioned as specific instances of that practice. This conception, analogous to “semantic unit” paradigms currently emerging in other information-centric domains, is then used to reinterpret several of journalism’s urgent problems. A research agenda for developing computational journalism as an editorial activity within an increasingly data-centric communication environment is proposed, and several implications of the conception are discussed.
Atomising the News: The (In)Flexibility of Structured Journalism
Rhianne Jones & Bronwyn Jones
Click here to see Rhianne’s und Bronwyn’s abstract
The field of data-driven news production and delivery is maturing, and public service media are among a wide range of news organisations innovating to exploit these advances. This article extends the literature on computational journalism by analysing two of the BBC’s recent experiments in “atomizing” the news–an object-based approach, which seeks to make news more adaptable and scalable using media components that can be automatically and algorithmically combined in multiple ways. Findings suggest atomised news is viewed by the organisation as offering opportunities for greater efficiency and personalisation and sits within a broader turn towards “structured journalism.” We highlight three characteristics of atomisation—recording, recombining and re-use—to illustrate how it breaks from traditional approaches. We find journalists are “writing for machines” by converting unstructured information into structured data to enable automated recombination and future re-use of content. This impacts editorial control by delegating responsibility to either the algorithm or the audience, in the name of choice. We propose a research agenda that maps the field of structured journalism, contextualises it in the politics of data and technology, and further considers the implications for public service journalism.
Our special issue also features commentaries from two great colleagues:
Towards a Design Orientation on Algorithms and Automation in News Production
Click here to see Nick’s abstract
This essay responds to the articles in the special issue on “Algorithms, Automation, and News” by reflecting on two thematic threads apparent in the collection: (1) the role of journalistic values in technology, and (2) the hybridization of human and machine effort in news production workflows. I argue that to make further progress in these areas journalism studies should establish a design orientation towards journalistic technology, and seek to develop rigorous evaluation criteria and metrics that can help propel both scholarship and practice forward.
Prioritizing the Audience’s View of Automation in Journalism
Andrea L. Guzman
Click here to see Andrea’s abstract
This commentary for the special issue on the automation of journalism highlights the progress made in this area of study before advocating for researchers to pay greater attention to the audience and its perceptions of the technologies of automation, including algorithms, artificial intelligence, chatbots, recommender and personalization systems, and automated news-writing software.