This book examines the consequences of legislators' strategic communication for representation, demonstrating how legislators present their work to cultivate constituent support. Using new statistical techniques to analyze massive data sets, Justin Grimmer makes the compelling case that to understand political representation, we must understand what legislators say to constituents.

A guide for using computational text analysis to learn about the social world 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 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. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry

Constituents often fail to hold their representatives accountable for federal spending decisions—even though those very choices have a pervasive influence on American life. Why does this happen? Breaking new ground in the study of representation, The Impression of Influence demonstrates how legislators skillfully inform constituents with strategic communication and how this facilitates or undermines accountability. Using a massive collection of Congressional texts and innovative experiments and methods, the book shows how legislators create an impression of influence through credit claiming messages. Anticipating constituents' reactions, legislators claim credit for programs that elicit a positive response, making constituents believe their legislator is effectively representing their district. This spurs legislators to create and defend projects popular with their constituents. Yet legislators claim credit for much more—they announce projects long before they begin, deceptively imply they deserve credit for expenditures they had little role in securing, and boast about minuscule projects. Unfortunately, legislators get away with seeking credit broadly because constituents evaluate the actions that are reported, rather than the size of the expenditures. The Impression of Influence raises critical questions about how citizens hold their political representatives accountable and when deception is allowable in a democracy.

Many people attempt to discover useful information by reading large quantities of unstructured text, but because of known human limitations even experts are ill-suited to succeed at this task. This difficulty has inspired the creation of numerous automated cluster analysis methods to aid discovery. We address two problems that plague this literature. First, the optimal use of any one of these methods requires that it be applied only to a specific substantive area, but the best area for each method is rarely discussed and usually unknowable ex ante. We tackle this problem with mathematical, statistical, and visualization tools that define a search space built from the solutions to all previously proposed cluster analysis methods (and any qualitative approaches one has time to include) and enable a user to explore it and quickly identify useful information. Second, in part because of the nature of unsupervised learning problems, cluster analysis methods are not routinely evaluated in ways that make them vulnerable to being proven suboptimal or less than useful in specific data types. We therefore propose new experimental designs for evaluating these methods. With such evaluation designs, we demonstrate that our computer-assisted approach facilitates more efficient and insightful discovery of useful information than either expert human coders using qualitative or quantitative approaches or existing automated methods. We (will) make available an easy-to-use software package that implements all our suggestions.