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This chapter examines how the multiplication of digital metrics, analytics, and algorithms is reconfiguring work practices and professional identities. It focuses on the case of journalism, a field that has been profoundly changed by digital technologies. It describes modern newsrooms that use digital tools in the gathering, production, and diffusion of information on the web, from group chats to social media platforms and content management systems. The chapter also introduces a new market that emerged for “web analytics” or software programs that track the behaviour and preferences of internet users. It describes how editors and journalists are provided with a constant stream of data about their audience, receiving increasingly detailed information in real time about the number of visitors, comments, likes, and tweets that their articles attract.

This chapter provides the structural and historical background for the analysis of web analytics. It traces the distinct relationships and quantitative modes of representation that developed between journalists and their publics over the course of the past century and a half. It also relates developments from the different trajectories of the journalistic field in the United States and France. The chapter offers a comparative genealogy of how the relationship between journalists and their readers shapes current interpretations and debates about web analytics. It also argues how impossible it is to separate the question of publics from the broader and conflicting forces that shape journalistic field.

This chapter explores the implications of web analytics for further studies of digital metrics beyond the case of journalism. At a time when nearly every domain is affected by analytics and algorithms, the chapter also provides an overview of what kinds of changes are to be expected and what should not be taken for granted whenever metrics take over. It describes how online media became a different place following the election of Donald Trump as the forty-fifth president of the United States in which news organizations and digital platforms entered into a political and economic maelstrom. It investigates the moral panic surrounding the uncovering of “content farms” and the stream of tweets from the White House labelling mainstream news organizations as “fake news” that caused the media ecosystem to become the center of new controversies about the future of information and democracy. The chapter also shows how news websites can bear some responsibility for problematic developments in journalism.

This chapter introduces non-computational scientists to the general field of machine learning and its methods. The chapter begins by outlining the common structure of machine learning applications, and then it highlights some of the most effective machine learning methods. Then there is a discussion of a case study at the intersection of machine learning and epidemiology: Google Flu Tends. At the end of the chapter there are steps given on how to begin creating practical machine learning algorithms for population health. In so doing this chapter sets the stage for the reader to become familiar with machine modeling as a tool for population health.