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      Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data

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          Abstract

          Are legislators responsive to the priorities of the public? Research demonstrates a strong correspondence between the issues about which the public cares and the issues addressed by politicians, but conclusive evidence about who leads whom in setting the political agenda has yet to be uncovered. We answer this question with fine-grained temporal analyses of Twitter messages by legislators and the public during the 113th US Congress. After employing an unsupervised method that classifies tweets sent by legislators and citizens into topics, we use vector autoregression models to explore whose priorities more strongly predict the relationship between citizens and politicians. We find that legislators are more likely to follow, than to lead, discussion of public issues, results that hold even after controlling for the agenda-setting effects of the media. We also find, however, that legislators are more likely to be responsive to their supporters than to the general public.

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          Macroeconomics and Reality

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            Effects of Public Opinion on Policy

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              Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

              Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.
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                Author and article information

                Journal
                Am Polit Sci Rev
                Am Polit Sci Rev
                APSR
                The American Political Science Review
                Cambridge University Press
                0003-0554
                1537-5943
                12 July 2019
                : 113
                : 4
                : 883-901
                Affiliations
                [1 ]University of Southern California
                [2 ]New York University
                Author notes
                Pablo Barberá, Assistant Professor, School of International Relations, University of Southern California, pbarbera@ 123456usc.edu . Andreu Casas, Moore-Sloan Research Fellow, Center for Data Science, New York University, andreucasas@ 123456nyu.edu . Jonathan Nagler, Professor, Wilf Family Department of Politics, New York University, jonathan.nagler@ 123456nyu.edu . Patrick J.Egan, Associate Professor, Wilf Family Department of Politics, New York University, patrick.egan@ 123456nyu.edu . Richard Bonneau, Professor, Center For Genomics and Systems Biology, Courant Institute of Mathematical Sciences, Computer Science Department, and Center for Data Science, New York University; and Flatiron Institute, Center for Computational Biology, Simons Foundation, bonneau@ 123456nyu.edu . John T. Jost, Professor, Department of Psychology, New York University, john.jost@ 123456nyu.edu . Joshua A. Tucker, Professor, Wilf Family Department of Politics, New York University, joshua.tucker@ 123456nyu.edu .

                We thank Nick Beauchamp, Ken Benoit, Drew Dimmery, Andrew Eggers, Thorsten Faas, Michael Lewis-Beck, Jennifer Pan, Paul Quirk, Molly Roberts, Annelise Russell, Gaurav Sood, David Sontag, Dustin Tingley, and John Wilkerson for their helpful comments and suggestions to previous versions of this paper. We also gratefully acknowledge financial support for the NYU Social Media and Political Participation (SMaPP) lab from the INSPIRE program of the National Science Foundation (Award #1248077), the Bill and Melinda Gates Foundation, the William and Flora Hewlett Foundation, the Rita Allen Foundation, the John S. and James L. Knight Foundation, and Intel. In addition, we would like to thank the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation for their support of the Moore Sloan Data Science Environment, which funded Casas’ time on the project. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/AA96D2

                Author information
                http://orcid.org/0000-0002-9063-4829
                http://orcid.org/0000-0001-6974-3652
                http://orcid.org/0000-0001-6918-9428
                http://orcid.org/0000-0002-9905-7466
                http://orcid.org/0000-0002-2844-4645
                http://orcid.org/0000-0003-1321-8650
                Article
                APSR-113-04-883
                10.1017/S0003055419000352
                7672368
                33303996
                4ee12ed7-5c8e-49e3-a782-ab66016cc956
                © 2019 American Political Science Association

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 July 2018
                : 31 January 2019
                : 13 May 2019
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