Data has become a social and political issue because of its capacity to reconfigure relationships between states, subjects, and citizens. This book explores how data has acquired such an important capacity and examines how critical interventions in its uses in both theory and practice are possible. Data and politics are now inseparable: data is not only shaping our social relations, preferences and life chances but our very democracies. Expert international contributors consider political questions about data and the ways it provokes subjects to govern themselves by making rights claims. Concerned with the things (infrastructures of servers, devices, and cables) and language (code, programming, and algorithms) that make up cyberspace, this book demonstrates that without understanding these conditions of possibility it is impossible to intervene in or to shape data politics. Aimed at academics and postgraduate students interested in political aspects of data, this volume will also be of interest to experts in the fields of internet studies, international studies, Big Data, digital social sciences and humanities. The Open Access version of this book, available at https://www.routledge.com/Data-Politics-Worlds-Subjects-Rights/Bigo-Isin-Ruppert/p/book/9781138053267, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Introduction to data analysis; Predictions and projections: some issues of research design; Two-variable linear regression; Multiple regression.

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.

The importance of social media as a way to monitor an electoral campaign is well established. Day-by-day, hour-by-hour evaluation of the evolution of online ideas and opinion allows observers and scholars to monitor trends and momentum in public opinion well before traditional polls. However, there are difficulties in recording and analyzing often brief, unverified comments while the unequal age, gender, social and racial representation among social media users can produce inaccurate forecasts of final polls. Reviewing the different techniques employed using social media to nowcast and forecast elections, this book assesses its achievements and limitations while presenting a new technique of "sentiment analysis" to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media-based electoral forecasts prove most accurate while new case studies from France, the United States and Italy demonstrate how much more accurate "sentiment analysis" can prove.

How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.

Provides academics, journalists, and general readers with bird's-eye view of data-driven practices and their impact in politics and media.

Given the advanced state of digital technology and social media, one would think that the Democratic and Republican Parties would be reasonably well-matched in terms of their technology uptake and sophistication. But as past presidential campaigns have shown, this is not the case. So what explains this odd disparity? Political scientists have shown that Republicans effectively used the strategy of party building and networking to gain campaign and electoral advantage throughout the twentieth century. In Prototype Politics, Daniel Kreiss argues that contemporary campaigning has entered a new technology-intensive era that the Democratic Party has engaged to not only gain traction against the Republicans, but to shape the new electoral context and define what electoral participation means in the twenty-first century. Prototype Politics provides an analytical framework for understanding why and how campaigns are newly "technology-intensive," and why digital media, data, and analytics are at the forefront of contemporary electoral dynamics. The book discusses the importance of infrastructure, the contexts within which technological innovation happens, and how the collective making of prototypes shapes parties and their technological futures. Drawing on an analysis of the careers of 629 presidential campaign staffers from 2004-2012, as well as interviews with party elites on both sides of the aisle, Prototype Politics details how and why the Democrats invested more in technology, were able to attract staffers with specialized expertise to work in electoral politics, and founded an array of firms to diffuse technological innovations down ballot and across election cycles. Taken together, this book shows how the differences between the major party campaigns on display in 2012 were shaped by their institutional histories since 2004, as well as that of their extended network of allied organizations. In the process, this book argues that scholars need to understand how technological development around politics happens in time and how the dynamics on display during presidential cycles are the outcome of longer processes.