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Computational Analysis of Communication - by Wouter Van Atteveldt & Damian Trilling & Carlos Arcila Calderon (Paperback)
About this item
Highlights
- Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing.
- About the Author: Dr. Wouter van Atteveldt is an Associate Professor of Political Communication at Vrije Universiteit, Amsterdam.
- 336 Pages
- Social Science, Research
Description
About the Book
"The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before"--Book Synopsis
Provides clear guidance on leveraging computational techniques to answer social science questions
In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance.
Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors--with expertise in both the social sciences and computer science--explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource:
- Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others
- Integrates theory, methods, and tools to provide unified approach to the subject
- Includes sample code in Python and links to actual research questions and cases from social science and communication studies
- Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science
- Developed in partnership with the International Communication Association and by the editors of Computational Communication Research
Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
From the Back Cover
A practical introduction to the analysis of texts, networks, and images with code examples in Python and R
In disciplines such as political science, sociology, psychology, communication science, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing -discipline-relevant examples, clear explanations, and practical guidance.
Assuming little or no background in data science or computational linguistics, this accessible textbook teaches readers how to use state-of-the-art computational methods to perform data-driven analyses of social science issues. A team of authors with expertise in both the social sciences and computer science explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource:
- Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others
- Integrates theory, methods, and tools to provide a unified approach to the subject
- Includes sample code in Python and R and links to actual research questions and cases from social science and communication studies
- Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science
Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
About the Author
Dr. Wouter van Atteveldt is an Associate Professor of Political Communication at Vrije Universiteit, Amsterdam. He is co-founder of the Computational Methods division of the International Communication Association, and Founding Chief Editor of Computational Communication Research. He has published extensively on innovative methods for analyzing political text and contributed to a number of relevant R and Python packages.
Dr. Damian Trilling is an Associate Professor, Department of Communication Science, at the University of Amsterdam, and Associate Editor of Computational Communication Research. His research uses computational methods such as the analysis of digital trace data and large-scale text analysis to study the use and effects of news media. He has developed extensive teaching materials to introduce social scientists to the Python programming language.
Dr. Carlos Arcila Calderón is an Associate Professor, Department of Sociology and Communication, at the University of Salamanca, Chief Editor of the journal Disertaciones, and member of the Editorial Board of Computational Communication Research. He has published extensively on new media and social media studies, and has led the prototype Autocop, a Spark-based environment to run distributed supervised sentiment analysis of Twitter messages.