From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.

Text as Data: A New Framework for Machine Learning and the Social Sciences (Princeton UP, 2022) is organized around the core tasks in research projects using text--representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.

Bridging many divides--computer science and social science, the qualitative and the quantitative, and industry and academia--Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.

Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy.