Data Journalism Handbook 2

When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links

This book offers an interdisciplinary introduction to data journalism, offering a unique combination of critical reflection and practical insight into the field, including how data journalism is done around the world and the broader consequences of datafication in the news.

The Data Journalism Handbook: Towards a Critical Data Practice provides a rich and panoramic introduction to data journalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field. With more than 50 chapters from leading researchers and practitioners of data journalism, it explores the work needed to render technologies and data productive for journalistic purposes. It also gives a 'behind the scenes' look at the social lives of datasets, data infrastructures, and data stories in newsrooms, media organizations, startups, civil society organizations and beyond. The book includes sections on 'doing issues with data', 'assembling data', 'working with data', 'experiencing data', 'investigating data, platforms and algorithms', 'organizing data journalism', 'learning data journalism together' and 'situating data journalism'.

How do we practice journalism in a digital world, in which the old 'rules' no longer apply? This text offers comprehensive, instructive coverage of the techniques and secrets of being a successful online journalist, both from a theoretical and practical point of view. Reflecting the vitality of the web, it will inspire you to acquire new skills and make sense of a transforming industry. Key Features: How to investigate and break stories online Learn to broadcast to millions using video and podcast How to blog like a pro Learn to manage and stimulate user-generated content Include and use social media in your toolkit How to dig out stories using data journalism Rise to the challenge of citizen journalism Make your journalism more interactive at every stage of the process Dedicated chapter for Law and Online Communication The Online Journalism Handbook is essential reading for all journalism students and professionals and of key interest to media, communication studies and more broadly the social sciences.

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.