The Algorithmic Distribution Of News

Download or read book The Algorithmic Distribution of News written by James Meese and published by Springer Nature. This book was released on 2022-09-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores how governments, policymakers and newsrooms have responded to the algorithmic distribution of the news. Contributors analyse the ongoing battle between platforms and publishers, evaluate recent attempts to manage these tensions through policy reform and consider whether algorithms can be regulated to promote media diversity and stop misinformation and hate speech. Chapter authors also interview journalists and find out how their work is changing due to the growing importance of algorithmic systems. Drawing together an international group of scholars, the book takes a truly global perspective offering case studies from Switzerland, Germany, Kenya, New Zealand, Canada, Australia, and China. The collection also provides a series of critical analyses of recent policy developments in the European Union and Australia, which aim to provide a more secure revenue base for news media organisations. A valuable resource for journalism and policy scholars and students, Governing the Algorithmic Distribution of News is an important guide for anyone hoping to understand the central regulatory issues surrounding the online distribution of news.

Download or read book Algorithms, Automation, and News written by Neil Thurman and published by Routledge. This book was released on 2021-05-18 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automating the News written by Nicholas Diakopoulos and published by Harvard University Press. This book was released on 2019-06-10 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. An expert in computer science and media explains the present and future of a world in which news is created by algorithm. Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories—increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism. Newsbots converse with social media audiences, distributing stories and receiving feedback. Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences. Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate. But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation. Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced. With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news. The human–algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.

Download or read book Social Media and the Public Interest written by Philip M. Napoli and published by Columbia University Press. This book was released on 2019-08-27 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Facebook, a platform created by undergraduates in a Harvard dorm room, has transformed the ways millions of people consume news, understand the world, and participate in the political process. Despite taking on many of journalism’s traditional roles, Facebook and other platforms, such as Twitter and Google, have presented themselves as tech companies—and therefore not subject to the same regulations and ethical codes as conventional media organizations. Challenging such superficial distinctions, Philip M. Napoli offers a timely and persuasive case for understanding and governing social media as news media, with a fundamental obligation to serve the public interest. Social Media and the Public Interest explores how and why social media platforms became so central to news consumption and distribution as they met many of the challenges of finding information—and audiences—online. Napoli illustrates the implications of a system in which coders and engineers drive out journalists and editors as the gatekeepers who determine media content. He argues that a social media–driven news ecosystem represents a case of market failure in what he calls the algorithmic marketplace of ideas. To respond, we need to rethink fundamental elements of media governance based on a revitalized concept of the public interest. A compelling examination of the intersection of social media and journalism, Social Media and the Public Interest offers valuable insights for the democratic governance of today’s most influential shapers of news.

Download or read book Media Technologies written by Tarleton Gillespie and published by MIT Press. This book was released on 2014-01-24 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scholars from communication and media studies join those from science and technology studies to examine media technologies as complex, sociomaterial phenomena. In recent years, scholarship around media technologies has finally shed the assumption that these technologies are separate from and powerfully determining of social life, looking at them instead as produced by and embedded in distinct social, cultural, and political practices. Communication and media scholars have increasingly taken theoretical perspectives originating in science and technology studies (STS), while some STS scholars interested in information technologies have linked their research to media studies inquiries into the symbolic dimensions of these tools. In this volume, scholars from both fields come together to advance this view of media technologies as complex sociomaterial phenomena. The contributors first address the relationship between materiality and mediation, considering such topics as the lived realities of network infrastructure. The contributors then highlight media technologies as always in motion, held together through the minute, unobserved work of many, including efforts to keep these technologies alive. Contributors Pablo J. Boczkowski, Geoffrey C. Bowker, Finn Brunton, Gabriella Coleman, Gregory J. Downey, Kirsten A. Foot, Tarleton Gillespie, Steven J. Jackson, Christopher M. Kelty, Leah A. Lievrouw, Sonia Livingstone, Ignacio Siles, Jonathan Sterne, Lucy Suchman, Fred Turner

Download or read book How Algorithms Create and Prevent Fake News written by Noah Giansiracusa and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what's real and what's not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning--especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what's at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information today is filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias - which gets amplified in harmful data feedback loops. Don't be afraid: with this book you'll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope. .

Download or read book Sharing News Online written by Fiona Martin and published by Springer. This book was released on 2019-07-16 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the political economics and cultural politics of social media news sharing, investigating how it is changing journalism and the news media internationally. News sharing plays important economic and cultural roles in an attention economy, recommending the stories audiences find valuable, making them more visible, and promoting the digital platforms that are reshaping our media ecologies. But is news sharing a force for democracy, or a sign of journalism’s declining power to set news agendas? In Sharing News Online, Tim Dwyer and Fiona Martin analyse the growth of commendary culture and the business of social news, critique the rise of news analytics and dissect virality online. They reveal that surprisingly, we share political stories more highly than celebrity news, and they probe how deeply affect drives our sharing behaviour. In mapping the contours of a critical digital media phenomenon, this book makes essential reading for scholars, journalists and media executives.