Chris J. Vargo – författare

Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Python code to traverse the vast expanses of data—significantly enhancing productivity without compromising scholarly integrity. We’ll survey a wide array of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labeled accurately and precisely. By reading this book, you should leave with an understanding of how to select the best computational content analysis methodology to your needs for the data and problem you have.This guide gives researchers the tools they need to amplify their analytical reach through the integration of content analysis with computational classification approaches, including machine learning and the latest advancements in generative artificial intelligence (AI) and large language models (LLMs). It is particularly useful for academic researchers looking to classify media data and advanced scholars in mass communications research, media studies, digital communication, political communication, and journalism.Complementing the book are online resources: datasets for practice, Python code scripts, extended exercise solutions, and practice quizzes for students, as well as test banks and essay prompts for instructors. Please visit www.routledge.com/9781032846354.

Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Python code to traverse the vast expanses of data—significantly enhancing productivity without compromising scholarly integrity. We’ll survey a wide array of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labeled accurately and precisely. By reading this book, you should leave with an understanding of how to select the best computational content analysis methodology to your needs for the data and problem you have.This guide gives researchers the tools they need to amplify their analytical reach through the integration of content analysis with computational classification approaches, including machine learning and the latest advancements in generative artificial intelligence (AI) and large language models (LLMs). It is particularly useful for academic researchers looking to classify media data and advanced scholars in mass communications research, media studies, digital communication, political communication, and journalism.Complementing the book are online resources: datasets for practice, Python code scripts, extended exercise solutions, and practice quizzes for students, as well as test banks and essay prompts for instructors. Please visit www.routledge.com/9781032846354.

Agendamelding: News, Social Media, Audiences, and Civic Community builds on the premise that people construct civic community from the information that they seek—as well as the information that seeks them—to trace the processes by which we mix, or meld, agendas from various sources into a coherent picture of the civic community in which we live. Using the presidential elections of 2008, 2012, and 2016, this book tests a formula that allows us to predict how potential voters lean towards communities in which they feel comfortable—for example, Republican, Democratic, or Independent. These analyses take into account differences in the use of traditional news media vs. social media among media consumers, as well as varying levels of press freedom across national populations.

Agendamelding: News, Social Media, Audiences, and Civic Community builds on the premise that people construct civic community from the information that they seek—as well as the information that seeks them—to trace the processes by which we mix, or meld, agendas from various sources into a coherent picture of the civic community in which we live. Using the presidential elections of 2008, 2012, and 2016, this book tests a formula that allows us to predict how potential voters lean towards communities in which they feel comfortable—for example, Republican, Democratic, or Independent. These analyses take into account differences in the use of traditional news media vs. social media among media consumers, as well as varying levels of press freedom across national populations.

From the viewpoint of newspaper organizations the main competitive media has shrunk to only one, the internet. But the effect of this innovation has been devastating in capturing the vast majority of the advertising revenues on which newspapers have depended. The larger the internet-based media became the more newspapers and other media shrank. Pairing an academic and former industry news manager, this textbook assesses the situation in which the regional news media industry finds itself, and explores methods, processes and techniques, which might usefully be introduced to help the news media firm secure a viable future. In focusing on newspapers, magazines, TV and radio, the work is filled with real-life examples and interviews with news media managers, illustrating how management is being conducted in this age of turbulence. The goal is to give students practice in solving complex strategic problems and to provide them with a series of intellectual and professional exercises. Their method of using case studies will enable students to explore in detail key theoretical issues before applying them to real life management settings.