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Brandon M. Stewart
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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
J Grimmer, BM Stewart
Political Analysis 21 (3), 267-297, 2013
37142013
Structural topic models for open-ended survey responses
ME Roberts, BM Stewart, D Tingley, C Lucas, J Leder-Luis, S Gadarian, ...
American Journal of Political Science 58 (4), 1064-1082, 2014
19722014
stm: R Package for Structural Topic Models
ME Roberts, BM Stewart, D Tingley
Journal of Statistical Software 91 (2), 1-40, 2019
16332019
A model of text for experimentation in the social sciences
ME Roberts, BM Stewart, E Airoldi
Journal of the American Statistical Association 111 (515), 988-1003, 2016
7642016
Computer-assisted text analysis for comparative politics
C Lucas, RA Nielsen, ME Roberts, BM Stewart, A Storer, D Tingley
Political Analysis 23 (2), 254-277, 2015
6282015
The structural topic model and applied social science
ME Roberts, BM Stewart, D Tingley, EM Airoldi
Advances in neural information processing systems workshop on topic models ¡¦, 2013
5012013
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
AJB Chaney, BM Stewart, BE Engelhardt
Proceedings of the 12th ACM conference on recommender systems, 224-232, 2018
3202018
Measuring the predictability of life outcomes with a scientific mass collaboration
MJ Salganik, I Lundberg, AT Kindel, CE Ahearn, K Al-Ghoneim, ...
Proceedings of the National Academy of Sciences 117 (15), 8398-8403, 2020
2692020
What is your estimand? Defining the target quantity connects statistical evidence to theory
I Lundberg, R Johnson, BM Stewart
American Sociological Review 86 (3), 532-565, 2021
2172021
Machine learning for social science: An agnostic approach
J Grimmer, ME Roberts, BM Stewart
Annual Review of Political Science 24, 395-419, 2021
2162021
Text as data: A new framework for machine learning and the social sciences
J Grimmer, ME Roberts, BM Stewart
Princeton University Press, 2022
2142022
Navigating the local modes of big data: The case of topic models
M Roberts, B Stewart, D Tingley
Computational Social Science: Discovery and Prediction, 2016
214*2016
How to make causal inferences using texts
N Egami, CJ Fong, J Grimmer, ME Roberts, BM Stewart
Science Advances 8 (42), eabg2652, 2022
1812022
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ...
Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022
1692022
Adjusting for confounding with text matching
ME Roberts, BM Stewart, RA Nielsen
American Journal of Political Science 64 (4), 887-903, 2020
136*2020
Choosing Your Neighbors: Networks of Diffusion in International Relations
YM Zhukov, BM Stewart
International Studies Quarterly 57 (2), 271-287, 2013
1212013
The global diffusion of law: Transnational crime and the case of human trafficking
BA Simmons, P Lloyd, BM Stewart
International Organization 72 (2), 249-281, 2018
120*2018
A la carte embedding: Cheap but effective induction of semantic feature vectors
M Khodak, N Saunshi, Y Liang, T Ma, B Stewart, S Arora
Proceedings of the 56th Annual Meeting of the Association for Computational ¡¦, 2018
1102018
Computer-assisted reading and discovery for student generated text in massive open online courses
J Reich, DH Tingley, J Leder-Luis, ME Roberts, B Stewart
Journal of Learning Analytics 2 (1), 156-184, 2015
992015
Use of force and civil–military relations in Russia: an automated content analysis
BM Stewart, YM Zhukov
Small Wars & Insurgencies 20 (2), 319-343, 2009
96*2009
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