Brandon M. Stewart
Zitiert von
Zitiert von
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
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
stm: R Package for Structural Topic Models
ME Roberts, BM Stewart, D Tingley
Journal of Statistical Software 91 (2), 1-40, 2019
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
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
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
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
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
Text as data: A new framework for machine learning and the social sciences
J Grimmer, ME Roberts, BM Stewart
Princeton University Press, 2022
Machine learning for social science: An agnostic approach
J Grimmer, ME Roberts, BM Stewart
Annual Review of Political Science 24, 395-419, 2021
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
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
How to make causal inferences using texts
N Egami, CJ Fong, J Grimmer, ME Roberts, BM Stewart
Science Advances 8 (42), eabg2652, 2022
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
Adjusting for confounding with text matching
ME Roberts, BM Stewart, RA Nielsen
American Journal of Political Science 64 (4), 887-903, 2020
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
Choosing Your Neighbors: Networks of Diffusion in International Relations
YM Zhukov, BM Stewart
International Studies Quarterly 57 (2), 271-287, 2013
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
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
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
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