@online{AIKeJiDaBenYing2018,
  title = {微信成功挑战10亿人聊天记录的背后，核心技术原来是它},
  author = {{AI科技大本营}},
  date = {2018-04-27},
  urldate = {2026-01-12},
  abstract = {翻译 \textbar{} AI科技大本营（rgznai100） 参与 \textbar{} Shawn，周翔 作为数据库领域的顶级会议，VLDB (Very Large Data Base)是数据库研究人员、供应商、参与者、应用开发者、以及用户一年一度的主要国际论坛。今年的 VLDB 于 8 月 28 日------9 月 1 日在德国慕尼黑举行，涵盖了数据管...},
  langid = {chinese},
  organization = {腾讯云开发者社区},
  file = {D:\zotero_system\storage\3LYFA573\1111436.html}
}

@inproceedings{ArunEtAl2010,
  title = {On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations},
  shorttitle = {On Finding the Natural Number of Topics with Latent Dirichlet Allocation},
  booktitle = {Advances in Knowledge Discovery and Data Mining},
  author = {Arun, R. and Suresh, V. and Veni Madhavan, C. E. and Narasimha Murthy, M. N.},
  editor = {Zaki, Mohammed J. and Yu, Jeffrey Xu and Ravindran, B. and Pudi, Vikram},
  date = {2010},
  series = {Lecture Notes in Computer Science},
  pages = {391--402},
  publisher = {Springer},
  location = {Berlin, Heidelberg},
  doi = {10.1007/978-3-642-13657-3_43},
  abstract = {It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet Allocation(LDA) as they determine the quality of features that are presented as features for classifiers like SVM. In this work we propose a measure to identify the correct number of topics and offer empirical evidence in its favor in terms of classification accuracy and the number of topics that are naturally present in the corpus. We show the merit of the measure by applying it on real-world as well as synthetic data sets(both text and images). In proposing this measure, we view LDA as a matrix factorization mechanism, wherein a given corpus C is split into two matrix factors M1 and M2 as given by Cd*w= M1d*tx Qt*w. Where d is the number of documents present in the corpus and w is the size of the vocabulary. The quality of the split depends on ``t'', the right number of topics chosen. The measure is computed in terms of symmetric KL-Divergence of salient distributions that are derived from these matrix factors. We observe that the divergence values are higher for non-optimal number of topics -- this is shown by a 'dip' at the right value for 't'.},
  isbn = {978-3-642-13657-3},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Arun et al2010_On Finding the Natural Number of Topics with Latent Dirichlet Allocation.pdf}
}

@article{AshEtAl2023,
  title = {Relatio: Text Semantics Capture Political and Economic Narratives},
  shorttitle = {Relatio},
  author = {Ash, Elliott and Gauthier, Germain and Widmer, Philine},
  date = {2023-04-28},
  journaltitle = {Political Analysis},
  pages = {1--18},
  publisher = {Cambridge University Press},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2023.8},
  urldate = {2023-05-19},
  abstract = {Social scientists have become increasingly interested in how narratives---the stories in fiction, politics, and life---shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our new software package relatio identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the U.S. Congressional Record to analyze political and economic narratives in recent decades. Our analysis highlights the dynamics, sentiment, polarization, and interconnectedness of narratives in political discourse.},
  langid = {english},
  file = {D\:\\Nutstore\\01_Literature\\Ash et al2023_Relatio.pdf;D\:\\Nutstore\\01_Literature\\Ash et al2023_Relatio3.pdf}
}

@article{AshHansen2023,
  title = {Text algorithms in economics},
  author = {Ash, Elliott and Hansen, Stephen},
  date = {2023},
  journaltitle = {Annual Review of Economics},
  pages = {Forthcoming},
  abstract = {This paper provides an overview of the methods used for algorithmic text analysis in economics, with a focus on three key contributions. First, the paper introduces methods for representing documents as high-dimensional count vectors over vocabulary terms, for representing words as vectors, and for representing word sequences as embedding vectors. Second, the paper defines four core empirical tasks that encompass most text-as-data research in economics, and enumerates the various approaches that have been taken so far for these tasks. Finally, the paper flags limitations in the current literature, with a focus on the challenge of validating algorithmic output.},
  langid = {italian},
  file = {D:\Nutstore\01_Literature\Ash_Hansen2023_Text Algorithms in Economics.pdf}
}

@inproceedings{BenderEtAl2021,
  title = {On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜},
  shorttitle = {On the Dangers of Stochastic Parrots},
  author = {Bender, Emily M. and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret},
  date = {2021-03-01},
  series = {FAccT '21},
  pages = {610--623},
  publisher = {Association for Computing Machinery},
  location = {New York, NY, USA},
  doi = {10.1145/3442188.3445922},
  urldate = {2026-01-20},
  abstract = {The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.},
  eventtitle = {2021 ACM Conference on Fairness, Accountability, and Transparency},
  isbn = {978-1-4503-8309-7},
  file = {D:\zotero_system\storage\DBSIZDEC\ BenderEtAl 2021_On the Dangers of Stochastic Parrots Can Language Models Be Too Big _ BenderEtAl 2021_On the Dangers of Stochastic Parrots Can Language Models Be Too Big .pdf}
}

@inproceedings{BengioEtAl2000,
  title = {A neural probabilistic language model},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Bengio, Yoshua and Ducharme, R\'ejean and Vincent, Pascal},
  date = {2000},
  volume = {13},
  publisher = {MIT Press},
  urldate = {2023-05-27},
  abstract = {A goal  of statistical language modeling is  to  learn  the joint probability  function of sequences of words.  This is intrinsically difficult because of  the curse of dimensionality:  we propose to fight it with its own weapons.  In the proposed approach one learns simultaneously (1) a distributed rep(cid:173) resentation for each word (i.e.  a similarity between words) along with (2)  the probability function for word sequences, expressed with these repre(cid:173) sentations.  Generalization is  obtained because a sequence of words that  has  never been seen before gets  high probability if it is  made of words  that are similar to words forming an already seen sentence.  We report on  experiments using neural networks for the probability function, showing  on  two  text  corpora that  the  proposed approach  very  significantly  im(cid:173) proves on a state-of-the-art trigram model.},
  langid = {catalan},
  file = {D:\Nutstore\01_Literature\Bengio et al2000_A Neural Probabilistic Language Model.pdf}
}

@article{BenoitEtAl2016,
  ids = {BenoitEtAl2016c},
  title = {Crowd-Sourced Text Analysis: Reproducible and Agile Production of Political Data},
  shorttitle = {Crowd-Sourced Text Analysis},
  author = {Benoit, Kenneth and Conway, Drew and Lauderdale, Benjamin E. and Laver, Michael and Mikhaylov, Slava},
  date = {2016-05},
  journaltitle = {American Political Science Review},
  volume = {110},
  number = {2},
  pages = {278--295},
  issn = {0003-0554, 1537-5943},
  doi = {10.1017/S0003055416000058},
  abstract = {Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of nonexperts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers' attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.},
  langid = {english},
  timestamp = {2019-05-30T02:39:34Z},
  file = {D:\Nutstore\01_Literature\Benoitet al2016_Crowd-Sourced Text Analysis - Reproducible and Agile Production of Political Data.pdf}
}

@article{BenoitEtAl2018,
  title = {Quanteda: An R Package for the Quantitative Analysis of Textual Data},
  shorttitle = {Quanteda},
  author = {Benoit, Kenneth and Watanabe, Kohei and Wang, Haiyan and Nulty, Paul and Obeng, Adam and M\"uller, Stefan and Matsuo, Akitaka},
  date = {2018-10-06},
  journaltitle = {Journal of Open Source Software},
  shortjournal = {JOSS},
  volume = {3},
  number = {30},
  pages = {774},
  issn = {2475-9066},
  doi = {10.21105/joss.00774},
  urldate = {2026-02-05},
  file = {D:\zotero_system\storage\QT42LB4D\ BenoitEtAl 2018_quanteda An R package for the quantitative analysis of textual data.pdf}
}

@incollection{BerryBerry2018,
  title = {Innovation and Diffusion Models in Policy Research},
  booktitle = {Theories of the Policy Process},
  author = {Berry, Frances Stokes and Berry, William D.},
  date = {2018},
  edition = {4},
  publisher = {Routledge},
  abstract = {This chapter reviews the dominant theories of government innovation in the public policy literature. It shows that these theories borrow heavily from others developed to explain innovative behavior by individuals: teachers using a new method of instruction, farmers adopting hybrid seeds and fertilizers, and consumers purchasing new products. The chapter explores that theories of government innovation share many commonalities with models that seek to explain organizational innovation. It deals with separate discussions of the central features of internal determinants and diffusion explanations for the adoption of a policy. The chapter explains three models that dominated early scholarship on policy innovation such as the national interaction model, the regional diffusion model and the leader-laggard model. It presents a model of American state lottery adoptions reflecting the simultaneous effects of both internal determinants and policy diffusion on state adoption behavior and employed event history analysis to test their model.},
  pagetotal = {45}
}

@article{Biber1993,
  title = {Representativeness in Corpus Design},
  author = {Biber, Douglas},
  date = {1993-01-01},
  journaltitle = {Literary and Linguistic Computing},
  shortjournal = {Lit Linguist Computing},
  volume = {8},
  number = {4},
  pages = {243--257},
  issn = {0268-1145},
  doi = {10.1093/llc/8.4.243},
  urldate = {2026-01-13},
  abstract = {The present paper addresses a number of issues related to achieving `representativeness' in linguistic corpus design, including: discussion of what it means to `represent' a language, definition of the target population, stratified versus proportional sampling of a language, sampling within texts, and issues relating to the required sample size (number of texts) of a corpus. The paper distinguishes among various ways that linguistic features can be distributed within and across texts; it analyses the distributions of several particular features, and it discusses the implications of these distributions for corpus design.The paper argues that theoretical research should be prior in corpus design, to identify the situational parameters that distinguish among texts in a speech community, and to identify the types of linguistic features that will be analysed in the corpus. These theoretical considerations should be complemented by empirical investigations of linguistic variation in a pilot corpus of texts, as a basis for specific sampling decisions. The actual construction of a corpus would then proceed in cycles: the original design based on theoretical and pilot-study analyses, followed by collection of texts, followed by further empirical investigations of linguistic variation and revision of the design.},
  file = {D:\zotero_system\storage\MW4GC446\8.4.html}
}

@article{BlackEtAl2011a,
  title = {Emotions, Oral Arguments, and Supreme Court Decision Making},
  author = {Black, Ryan C. and Treul, Sarah A. and Johnson, Timothy R. and Goldman, Jerry},
  date = {2011},
  journaltitle = {The Journal of Politics},
  volume = {73},
  number = {2},
  pages = {572--581},
  issn = {0022-3816},
  doi = {10.1017/s002238161100003x},
  abstract = {Students of linguistics and psychology demonstrate that word choices people make convey information about their emotions and thereby their intentions. Focusing on theory from these related fields we test whether the emotional content of Supreme Court justices' questions and comments made during oral arguments allow us to predict the decisions they make. Using aggregate data from all arguments between 1979 and 2008 and individual-level data from 2004 through 2008 we find justices' use of more unpleasant language towards the attorney representing one side of a case reduces the probability that side will prevail on the merits, both in terms of individual justices' votes and the overall case outcome.},
  timestamp = {2019-09-13T01:23:57Z},
  file = {D:\zotero_system\storage\85FJMT26\ BlackEtAl 2011_Emotions, oral arguments, and supreme court decision making.pdf}
}

@article{BlackwellEtAl2017,
  title = {A Unified Approach to Measurement Error and Missing Data: Overview and Applications},
  shorttitle = {A Unified Approach to Measurement Error and Missing Data},
  author = {Blackwell, Matthew and Honaker, James and King, Gary},
  date = {2017-08-01},
  journaltitle = {Sociological Methods \& Research},
  shortjournal = {Sociological Methods \& Research},
  volume = {46},
  number = {3},
  pages = {303--341},
  publisher = {SAGE Publications Inc},
  issn = {0049-1241},
  doi = {10.1177/0049124115585360},
  urldate = {2021-12-11},
  abstract = {Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion article with technical details and extensions.},
  langid = {english},
  file = {D:\zotero_system\storage\7MZFJ9WD\ BlackwellEtAl 2017_A unified approach to measurement error and missing data overview and applications.pdf}
}

@article{Blei2012,
  title = {Probabilistic Topic Models},
  author = {Blei, David M.},
  date = {2012-04},
  journaltitle = {Communications of the ACM},
  shortjournal = {Commun. ACM},
  volume = {55},
  number = {4},
  pages = {77--84},
  issn = {0001-0782, 1557-7317},
  doi = {10.1145/2133806.2133826},
  urldate = {2024-03-08},
  abstract = {Surveying a suite of algorithms that offer a solution to managing large document archives.},
  langid = {english}
}

@article{Blei2012a,
  title = {Topic Modeling and Digital Humanities Journal of Digital Humanities},
  author = {Blei, David M.},
  date = {2012},
  journaltitle = {Journal of Digital Humanities},
  volume = {2},
  number = {1},
  urldate = {2026-03-27},
  langid = {american},
  file = {D:\zotero_system\storage\7MIR6Q5E\topic-modeling-and-digital-humanities-by-david-m-blei.html}
}

@article{BleiEtAl2003,
  title = {Latent Dirichlet Allocation},
  author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
  date = {2003},
  journaltitle = {Journal of Machine Learning Research},
  volume = {3},
  pages = {993--1022},
  issn = {ISSN 1533-7928},
  urldate = {2019-02-10},
  issue = {Jan},
  timestamp = {2019-09-13T01:24:25Z},
  file = {D:\Nutstore\01_Literature\BleiLatent Dirichlet Allocation.pdf}
}

@article{Box1976,
  title = {Science and Statistics},
  author = {Box, George E. P.},
  date = {1976},
  journaltitle = {Journal of the American Statistical Association},
  volume = {71},
  number = {356},
  eprint = {2286841},
  eprinttype = {jstor},
  pages = {791--799},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2286841},
  urldate = {2026-01-20},
  abstract = {Aspects of scientific method are discussed: In particular, its representation as a motivated iteration in which, in succession, practice confronts theory, and theory, practice. Rapid progress requires sufficient flexibility to profit from such confrontations, and the ability to devise parsimonious but effective models, to worry selectively about model inadequacies and to employ mathematics skillfully but appropriately. The development of statistical methods at Rothamsted Experimental Station by Sir Ronald Fisher is used to illustrate these themes.},
  file = {D:\zotero_system\storage\B7Z8SR3R\ Box1976_Science and Statistics_ Box1976_Science and Statistics.pdf}
}

@article{CaoEtAl2009,
  title = {A Density-Based Method for Adaptive LDA Model Selection},
  author = {Cao, Juan and Xia, Tian and Li, Jintao and Zhang, Yongdong and Tang, Sheng},
  date = {2009-03-01},
  journaltitle = {Neurocomputing},
  shortjournal = {Neurocomputing},
  series = {Advances in Machine Learning and Computational Intelligence},
  volume = {72},
  number = {7},
  pages = {1775--1781},
  issn = {0925-2312},
  doi = {10.1016/j.neucom.2008.06.011},
  urldate = {2023-06-08},
  abstract = {Topic models have been successfully used in information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called ``topics''. However, it is important but difficult to select the appropriate number of topics for a specific dataset. In this paper, we study the inherent connection between the best topic structure and the distances among topics in Latent Dirichlet allocation (LDA), and propose a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.},
  langid = {english}
}

@article{Chaput-LangloisEtAl2024,
  title = {Multiple Imputation When Variables Exceed Observations: An Overview of Challenges and Solutions},
  shorttitle = {Multiple Imputation When Variables Exceed Observations},
  author = {Chaput-Langlois, Sophie and Stickley, Zachary L. and Little, Todd D. and Rioux, Charlie},
  editor = {Field, Sarahanne},
  date = {2024-02-28},
  journaltitle = {Collabra: Psychology},
  shortjournal = {Collabra: Psychology},
  volume = {10},
  number = {1},
  pages = {92993},
  issn = {2474-7394},
  doi = {10.1525/collabra.92993},
  urldate = {2026-02-06},
  abstract = {Missing data are a prevalent problem in psychological research that can reduce statistical power and bias parameter estimates. These problems can be mostly resolved with multiple imputation, a modern missing data treatment that is increasingly used. Imputation, however, requires the number of variables to be smaller than the number of observations (i.e., non-missing values), and this number is often exceeded due to, e.g., large assessments, high missing data rates, the inclusion of variables predictive of missing values, and the inclusion of non-linear transformations. Even when the ratio of variables to observations meets the minimum requirement, convergence failure can occur in large, complex models. Specialized techniques have been developed to overcome the challenges related to having too many variables in an imputation model, but they are still relatively unknown by researchers in psychology. Accordingly, this paper presents an overview of four imputation techniques that can be used to reduce the number of predictors in an imputation model: item aggregation with scales and parcels, passive imputation, principal component analysis (PcAux) and two-fold fully conditional specification. The purpose, advantages, limitations, and applications of each method are discussed, along with recommendations and illustrative examples, with the aims of (1) understanding different imputation methods and (2) identifying methods that could be useful for one's imputation problem.},
  keywords = {missing,multiple imputation},
  file = {D:\zotero_system\storage\7L5DJUC9\ Chaput-LangloisEtAl 2024_Multiple imputation when variables exceed observations An overview of challenges and solutions.pdf}
}

@article{Chatterjee2021,
  title = {A New Coefficient of Correlation},
  author = {Chatterjee, Sourav},
  date = {2021-10-02},
  journaltitle = {Journal of the American Statistical Association},
  volume = {116},
  number = {536},
  pages = {2009--2022},
  publisher = {Taylor \& Francis},
  issn = {0162-1459},
  doi = {10.1080/01621459.2020.1758115},
  urldate = {2022-10-30},
  abstract = {--Is it possible to define a coefficient of correlation which is (a) as simple as the classical coefficients like Pearson's correlation or Spearman's correlation, and yet (b) consistently estimates some simple and interpretable measure of the degree of dependence between the variables, which is 0 if and only if the variables are independent and 1 if and only if one is a measurable function of the other, and (c) has a simple asymptotic theory under the hypothesis of independence, like the classical coefficients? This article answers this question in the affirmative, by producing such a coefficient. No assumptions are needed on the distributions of the variables. There are several coefficients in the literature that converge to 0 if and only if the variables are independent, but none that satisfy any of the other properties mentioned above. Supplementary materials for this article are available online.},
  file = {D:\Nutstore\01_Literature\Chatterjee2021_A New Coefficient of Correlation.pdf}
}

@article{Coase1960,
  title = {The Problem of Social Cost},
  author = {Coase, R. H.},
  date = {1960},
  journaltitle = {The Journal of Law \& Economics},
  volume = {56},
  number = {4},
  eprint = {10.1086/674872},
  eprinttype = {jstor},
  pages = {837--877},
  publisher = {[The University of Chicago Press, The Booth School of Business, University of Chicago, The University of Chicago Law School]},
  issn = {0022-2186},
  doi = {10.1086/674872},
  urldate = {2023-05-27},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Coase2013_The Problem of Social Cost.pdf}
}

@article{ColliauEtAl2021,
  title = {MatLab vs. Python vs. R},
  author = {Colliau, Taylor and Rogers, Grace and Hughes, Zachariah and Ozgur, Ceyhun},
  date = {2021-03-07},
  journaltitle = {Journal of Data Science},
  volume = {15},
  number = {3},
  pages = {355--372},
  issn = {1680-743X, 1683-8602},
  doi = {10.6339/JDS.201707_15(3).0001},
  urldate = {2026-02-09},
  abstract = {Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics \& statistics. In today's data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment. MatLab can be used to teach introductory mathematics such as calculus and statistics. Both Python and R can be used to make decisions involving big data. On the one hand, Python is perfect for teaching introductory statistics in a data rich environment. On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.},
  langid = {english},
  file = {D:\zotero_system\storage\QK4QCBGH\ ColliauEtAl 2021_MatLab vs. python vs. R.pdf}
}

@online{Curry2026,
  title = {WeChat Revenue and Usage Statistics (2026)},
  author = {Curry, David},
  date = {2026-01-07},
  urldate = {2026-01-12},
  abstract = {WeChat was launched in 2011 as Weixin, Mandarin for `micro-message'. As the name suggests, it originally functioned as a simple messenger app, a Chinese equivalent to WhatsApp in essence. WeChat was developed by and belongs to Chinese tech behemoth Tencent, one of the most valuable companies in the world. Other names in the Tencent stable include QQ Messenger and Tencent Music. Though it started as a simple messenger platform, WeChat has become far more than that. Through its mini programs platform, WeChat has essentially become the home screen for many Chinese smartphone users, as almost every function can be completed from inside the app. Some of the most popular applications in China, including Pinduoduo, JD.com, DiDi and Meituan, all have mini programs on WeChat. Tencent},
  langid = {english},
  organization = {Business of Apps},
  file = {D:\zotero_system\storage\QC5MLZXH\wechat-statistics.html}
}

@article{DeveaudEtAl2014,
  title = {Accurate and Effective Latent Concept Modeling for Ad Hoc Information Retrieval},
  author = {Deveaud, Romain and SanJuan, Eric and Bellot, Patrice},
  date = {2014-04-30},
  journaltitle = {Document num\'erique},
  shortjournal = {Document num\'erique},
  volume = {17},
  number = {1},
  pages = {61--84},
  issn = {12795127},
  doi = {10.3166/dn.17.1.61-84},
  urldate = {2023-06-08},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Deveaud et al2014_Accurate and effective latent concept modeling for ad hoc information retrieval.pdf}
}

@article{DietrichEtAl2019,
  title = {Pitch Perfect: Vocal Pitch and the Emotional Intensity of Congressional Speech},
  author = {Dietrich, Bryce J. and Hayes, Matthew and O'Brien, Diana Z.},
  date = {2019},
  journaltitle = {American Political Science Review},
  pages = {Forthcoming},
  urldate = {2019-05-30},
  abstract = {Though audio archives are available for a number of political institutions, the data they provide receive scant attention from researchers. Yet, audio data offer important insights, including information about speakers' emotional states. Using one of the largest collections of natural audio ever compiled---74,158 Congressional floor speeches---we introduce a novel measure of legislators' emotional intensity: small changes in vocal pitch that are difficult for speakers to control. Applying our measure to MCs' floor speeches about women, we show that female MCs speak with greater emotional intensity when talking about women as compared to both their male colleagues and their speech on other topics. Our two supplementary analyses suggest that increased vocal pitch is consistent with legislators' broader issue commitments, and that emotionally intense speech may affect other lawmakers' behavior. More generally, by demonstrating the utility of audio-as-data approaches, our work highlights a new way of studying political speech.},
  timestamp = {2019-05-30T02:50:44Z},
  file = {D:\Nutstore\01_Literature\Dietrichet al2019_Pitch Perfect - Vocal Pitch and the Emotional Intensity of Congressional Speech.pdf}
}

@inproceedings{DodgeEtAl2021,
  title = {Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus},
  shorttitle = {Documenting Large Webtext Corpora},
  author = {Dodge, Jesse and Sap, Maarten and Marasovi\'c, Ana and Agnew, William and Ilharco, Gabriel and Groeneveld, Dirk and Mitchell, Margaret and Gardner, Matt},
  date = {2021-04-18},
  urldate = {2026-01-20},
  abstract = {Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.},
  eventtitle = {Conference on Empirical Methods in Natural Language Processing},
  langid = {english},
  file = {D:\zotero_system\storage\JFSH7SHJ\ DodgeEtAl 2021_Documenting Large Webtext Corpora A Case Study on the Colossal Clean Crawled Corpus_ DodgeEtAl 2021_Documenting Large Webtext Corpora A Case Study on the Colossal Clean Crawled Corpus.pdf}
}

@online{EgamiEtAl2024,
  title = {Using Imperfect Surrogates for Downstream Inference: Design-Based Supervised Learning for Social Science Applications of Large Language Models},
  shorttitle = {Using Imperfect Surrogates for Downstream Inference},
  author = {Egami, Naoki and Hinck, Musashi and Stewart, Brandon M. and Wei, Hanying},
  date = {2024-01-14},
  eprint = {2306.04746},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.2306.04746},
  urldate = {2026-02-09},
  abstract = {In computational social science (CSS), researchers analyze documents to explain social and political phenomena. In most scenarios, CSS researchers first obtain labels for documents and then explain labels using interpretable regression analyses in the second step. One increasingly common way to annotate documents cheaply at scale is through large language models (LLMs). However, like other scalable ways of producing annotations, such surrogate labels are often imperfect and biased. We present a new algorithm for using imperfect annotation surrogates for downstream statistical analyses while guaranteeing statistical properties -- like asymptotic unbiasedness and proper uncertainty quantification -- which are fundamental to CSS research. We show that direct use of surrogate labels in downstream statistical analyses leads to substantial bias and invalid confidence intervals, even with high surrogate accuracy of 80-90\%. To address this, we build on debiased machine learning to propose the design-based supervised learning (DSL) estimator. DSL employs a doubly-robust procedure to combine surrogate labels with a smaller number of high-quality, gold-standard labels. Our approach guarantees valid inference for downstream statistical analyses, even when surrogates are arbitrarily biased and without requiring stringent assumptions, by controlling the probability of sampling documents for gold-standard labeling. Both our theoretical analysis and experimental results show that DSL provides valid statistical inference while achieving root mean squared errors comparable to existing alternatives that focus only on prediction without inferential guarantees.},
  pubstate = {prepublished},
  file = {D:\zotero_system\storage\I72ITBJK\ EgamiEtAl 2024_Using imperfect surrogates for downstream inference Design-based supervised learning for social sci.pdf}
}

@article{EsareySumner2018,
  title = {Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate},
  shorttitle = {Marginal Effects in Interaction Models},
  author = {Esarey, Justin and Sumner, Jane Lawrence},
  date = {2018-08},
  journaltitle = {Comparative Political Studies},
  shortjournal = {Comparative Political Studies},
  volume = {51},
  number = {9},
  pages = {1144--1176},
  issn = {0010-4140, 1552-3829},
  doi = {10.1177/0010414017730080},
  urldate = {2022-12-29},
  abstract = {When a researcher suspects that the marginal effect of x on y varies with z, a common approach is to plot {$\partial$}y / {$\partial$}x at different values of z along with a pointwise confidence interval generated using the procedure described in Brambor, Clark, and Golder to assess the magnitude and statistical significance of the relationship. Our article makes three contributions. First, we demonstrate that the Brambor, Clark, and Golder approach produces statistically significant findings when {$\partial$}y / {$\partial$}x = 0 at a rate that can be many times larger or smaller than the nominal false positive rate of the test. Second, we introduce the interactionTest software package for R to implement procedures that allow easy control of the false positive rate. Finally, we illustrate our findings by replicating an empirical analysis of the relationship between ethnic heterogeneity and the number of political parties from Comparative Political Studies.},
  langid = {english},
  keywords = {interaction},
  file = {D:\Nutstore\01_Literature\Esarey_Sumner2018_Marginal Effects in Interaction Models - Determining and Controlling the False.pdf}
}

@article{EshimaEtAl2023,
  title = {Keyword-Assisted Topic Models},
  author = {Eshima, Shusei and Imai, Kosuke and Sasaki, Tomoya},
  date = {2023},
  journaltitle = {American Journal of Political Science},
  pages = {Early View},
  issn = {1540-5907},
  doi = {10.1111/ajps.12779},
  urldate = {2023-05-01},
  abstract = {In recent years, fully automated content analysis based on probabilistic topic models has become popular among social scientists because of their scalability. However, researchers find that these models often fail to measure specific concepts of substantive interest by inadvertently creating multiple topics with similar content and combining distinct themes into a single topic. In this article, we empirically demonstrate that providing a small number of keywords can substantially enhance the measurement performance of topic models. An important advantage of the proposed keyword-assisted topic model (keyATM) is that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our application, we find that keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics.},
  langid = {english},
  file = {D\:\\Nutstore\\01_Literature\\Eshima et al2023_Keyword-Assisted Topic Models.pdf;D\:\\Nutstore\\01_Literature\\Eshima et al2023_Keyword-Assisted Topic Models2.pdf}
}

@article{Firth1957,
  title = {A Synopsis of Linguistic Theory, 1930-1955},
  author = {Firth, John Rupert},
  date = {1957},
  journaltitle = {Studies in Linguistic Analysis},
  pages = {10--32},
  urldate = {2026-02-09},
  file = {D:\zotero_system\storage\5KFMWX96\1574231874045325568.html}
}

@book{FriedmanSchwartz2008,
  title = {A Monetary History of the United States, 1867-1960},
  author = {Friedman, Milton and Schwartz, Anna Jacobson},
  date = {2008-09-02},
  eprint = {Q7J_EUM3RfoC},
  eprinttype = {googlebooks},
  publisher = {Princeton University Press},
  abstract = {Writing in the June 1965 issue of theEconomic Journal, Harry G. Johnson begins with a sentence seemingly calibrated to the scale of the book he set himself to review: "The long-awaited monetary history of the United States by Friedman and Schwartz is in every sense of the term a monumental scholarly achievement--monumental in its sheer bulk, monumental in the definitiveness of its treatment of innumerable issues, large and small . . . monumental, above all, in the theoretical and statistical effort and ingenuity that have been brought to bear on the solution of complex and subtle economic issues." Friedman and Schwartz marshaled massive historical data and sharp analytics to support the claim that monetary policy--steady control of the money supply--matters profoundly in the management of the nation's economy, especially in navigating serious economic fluctuations. In their influential chapter 7, The Great Contraction--which Princeton published in 1965 as a separate paperback--they address the central economic event of the century, the Depression. According to Hugh Rockoff, writing in January 1965: "If Great Depressions could be prevented through timely actions by the monetary authority (or by a monetary rule), as Friedman and Schwartz had contended, then the case for market economies was measurably stronger." Milton Friedman won the Nobel Prize in Economics in 1976 for work related to A Monetary History as well as to his other Princeton University Press book, A Theory of the Consumption Function (1957).},
  isbn = {978-1-4008-2933-0},
  langid = {english},
  pagetotal = {889}
}

@incollection{Gabrielatos2018,
  title = {Keyness Analysis: Nature, Metrics and Techniques},
  shorttitle = {Keyness Analysis},
  booktitle = {Corpus Approaches to Discourse},
  author = {Gabrielatos, Costas},
  editor = {Taylor, Charlotte and Marchi, Anna},
  date = {2018},
  pages = {34--65},
  publisher = {Routledge},
  abstract = {This chapter discusses methodological issues relating to keyness analysis, and addresses a number of interconnected themes. It offers awareness of relevant},
  isbn = {978-1-315-17934-6},
  pagetotal = {34}
}

@book{GelmanEtAl2021,
  title = {Regression and Other Stories},
  author = {Gelman, Andrew and Hill, Jennifer and Vehtari, Aki},
  date = {2021},
  series = {Analytical Methods for Social Research},
  publisher = {Cambridge University Press},
  location = {Cambridge},
  doi = {10.1017/9781139161879},
  abstract = {Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting},
  isbn = {978-1-107-02398-7 978-1-139-16187-9 978-1-107-67651-0},
  langid = {english},
  pagetotal = {1}
}

@article{GilardiEtAl2023,
  title = {ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks},
  author = {Gilardi, Fabrizio and Alizadeh, Meysam and Kubli, Ma\"el},
  date = {2023-07-25},
  journaltitle = {Proceedings of the National Academy of Sciences},
  shortjournal = {Proc. Natl. Acad. Sci. U.S.A.},
  volume = {120},
  number = {30},
  pages = {e2305016120},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.2305016120},
  urldate = {2026-02-09},
  abstract = {Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (               n               = 6,183), we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by about 25 percentage points on average, while ChatGPT's intercoder agreement exceeds that of both crowd workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than \$0.003---about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.},
  langid = {english},
  file = {D:\zotero_system\storage\8KY8SYSM\ GilardiEtAl 2023_ChatGPT outperforms crowd workers for text-annotation tasks.pdf}
}

@article{GrahamEtAl2007,
  title = {How Many Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory},
  shorttitle = {How Many Imputations Are Really Needed?},
  author = {Graham, John W. and Olchowski, Allison E. and Gilreath, Tamika D.},
  date = {2007-09-01},
  journaltitle = {Prevention Science},
  shortjournal = {Prev Sci},
  volume = {8},
  number = {3},
  pages = {206--213},
  issn = {1573-6695},
  doi = {10.1007/s11121-007-0070-9},
  urldate = {2026-02-06},
  abstract = {Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most common approaches to missing data analysis. In theory, MI and FIML are equivalent when identical models are tested using the same variables, and when m, the number of imputations performed with MI, approaches infinity. However, it is important to know how many imputations are necessary before MI and FIML are sufficiently equivalent in ways that are important to prevention scientists. MI theory suggests that small values of m, even on the order of three to five imputations, yield excellent results. Previous guidelines for sufficient m are based on relative efficiency, which involves the fraction of missing information ({$\gamma$}) for the parameter being estimated, and m. In the present study, we used a Monte Carlo simulation to test MI models across several scenarios in which {$\gamma$} and m were varied. Standard errors and p-values for the regression coefficient of interest varied as a function of m, but not at the same rate as relative efficiency. Most importantly, statistical power for small effect sizes diminished as m became smaller, and the rate of this power falloff was much greater than predicted by changes in relative efficiency. Based our findings, we recommend that researchers using MI should perform many more imputations than previously considered sufficient. These recommendations are based on {$\gamma$}, and take into consideration one's tolerance for a preventable power falloff (compared to FIML) due to using too few imputations.},
  langid = {english},
  keywords = {missing},
  file = {D:\zotero_system\storage\EL4L88WE\ GrahamEtAl 2007_How many imputations are really needed Some practical clarifications of multiple imputation theory.pdf}
}

@article{GriffithsSteyvers2004,
  title = {Finding Scientific Topics},
  author = {Griffiths, Thomas L. and Steyvers, Mark},
  date = {2004},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {101},
  pages = {5228--5235},
  publisher = {National Acad Sciences},
  issue = {suppl 1},
  owner = {Sammo},
  timestamp = {2019-10-29T01:14:50Z},
  file = {D:\Nutstore\01_Literature\Griffiths_Steyvers2004_Finding scientific topics.pdf}
}

@article{Grimmer2010,
  title = {A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases},
  shorttitle = {A Bayesian Hierarchical Topic Model for Political Texts},
  author = {Grimmer, Justin},
  date = {2010},
  journaltitle = {Political Analysis},
  volume = {18},
  number = {1},
  pages = {1--35},
  issn = {1047-1987, 1476-4989},
  doi = {10.1093/pan/mpp034},
  abstract = {Political scientists lack methods to efficiently measure the priorities political actors emphasize in statements. To address this limitation, I introduce a statistical model that attends to the structure of political rhetoric when measuring expressed priorities: statements are naturally organized by author. The expressed agenda model exploits this structure to simultaneously estimate the topics in the texts, as well as the attention political actors allocate to the estimated topics. I apply the method to a collection of over 24,000 press releases from senators from 2007, which I demonstrate is an ideal medium to measure how senators explain their work in Washington to constituents. A set of examples validates the estimated priorities and demonstrates their usefulness for testing theories of how members of Congress communicate with constituents. The statistical model and its extensions will be made available in a forthcoming free software package for the R computing language.},
  langid = {english},
  timestamp = {2019-05-30T02:36:53Z},
  file = {D:\Nutstore\01_Literature\Grimmer2010_A Bayesian Hierarchical Topic Model for Political Texts - Measuring Expressed.pdf}
}

@book{GrimmerEtAl2022,
  title = {Text as Data: A New Framework for Machine Learning and the Social Sciences},
  shorttitle = {Text as Data},
  author = {Grimmer, Justin and Roberts, Margaret E. and Stewart, Brandon M.},
  date = {2022-01-04},
  eprint = {dL40EAAAQBAJ},
  eprinttype = {googlebooks},
  publisher = {Princeton University Press},
  abstract = {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 dataResearch design for a world of data delugeExamples from across the social sciences and industry},
  isbn = {978-0-691-20799-5},
  langid = {english},
  pagetotal = {360}
}

@article{GrimmerStewart2013,
  ids = {grimmerₜext₂013},
  title = {Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts},
  author = {Grimmer, Justin and Stewart, Brandon M.},
  date = {2013},
  journaltitle = {Political Analysis},
  volume = {21},
  number = {3},
  pages = {267--297},
  publisher = {SPM-PMSAPSA},
  timestamp = {2019-10-29T01:14:50Z},
  file = {D:\zotero_system\storage\TXKTCZ9W\Grimmer_Stewart2013_Text as Data - The Promise and Pitfalls of Automatic Content Analysis Methods2_ GrimmerStewart2013_Text as data the promise and pitfalls of automatic content analysis methods for political texts.pdf}
}

@article{Halterman2025,
  title = {Synthetically Generated Text for Supervised Text Analysis},
  author = {Halterman, Andrew},
  date = {2025-07},
  journaltitle = {Political Analysis},
  shortjournal = {Polit. Anal.},
  volume = {33},
  number = {3},
  pages = {181--194},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2024.31},
  urldate = {2026-02-09},
  abstract = {Abstract             Large language models are a powerful tool for conducting text analysis in political science, but using them to annotate text has several drawbacks, including high cost, limited reproducibility, and poor explainability. Traditional supervised text classifiers are fast and reproducible, but require expensive hand annotation, which is especially difficult for rare classes. This article proposes using LLMs to generate synthetic training data for training smaller, traditional supervised text models. Synthetic data can augment limited hand annotated data or be used on its own to train a classifier with good performance and greatly reduced cost. I provide a conceptual overview of text generation, guidance on when researchers should prefer different techniques for generating synthetic text, a discussion of ethics, a simple technique for improving the quality of synthetic text, and an illustration of its limitations. I demonstrate the usefulness of synthetic training through three validations: synthetic news articles describing police responses to communal violence in India for training an event detection system, a multilingual corpus of synthetic populist manifesto statements for training a sentence-level populism classifier, and generating synthetic tweets describing the fighting in Ukraine to improve a named entity system.},
  langid = {english},
  file = {D:\zotero_system\storage\BZAJ6CSM\ Halterman2025_Synthetically generated text for supervised text analysis.pdf}
}

@article{HaltermanKeith2025,
  title = {Codebook Llms: Evaluating Llms as Measurement Tools for Political Science Concepts},
  shorttitle = {Codebook Llms},
  author = {Halterman, Andrew and Keith, Katherine A.},
  date = {2025-09-19},
  journaltitle = {Political Analysis},
  shortjournal = {Polit. Anal.},
  eprint = {2407.10747},
  eprinttype = {arXiv},
  eprintclass = {cs},
  pages = {1--17},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2025.10017},
  urldate = {2026-02-09},
  abstract = {Codebooks -- documents that operationalize concepts and outline annotation procedures -- are used almost universally by social scientists when coding political texts. To code these texts automatically, researchers are increasing turning to generative large language models (LLMs). However, there is limited empirical evidence on whether "off-the-shelf" LLMs faithfully follow real-world codebook operationalizations and measure complex political constructs with sufficient accuracy. To address this, we gather and curate three real-world political science codebooks -- covering protest events, political violence and manifestos -- along with their unstructured texts and human labels. We also propose a five-stage framework for codebook-LLM measurement: preparing a codebook for both humans and LLMs, testing LLMs' basic capabilities on a codebook, evaluating zero-shot measurement accuracy (i.e. off-the-shelf performance), analyzing errors, and further (parameter-efficient) supervised training of LLMs. We provide an empirical demonstration of this framework using our three codebook datasets and several pretrained 7-12 billion open-weight LLMs. We find current open-weight LLMs have limitations in following codebooks zero-shot, but that supervised instruction tuning can substantially improve performance. Rather than suggesting the "best" LLM, our contribution lies in our codebook datasets, evaluation framework, and guidance for applied researchers who wish to implement their own codebook-LLM measurement projects.},
  file = {D:\zotero_system\storage\UFMZYY74\ HaltermanKeith2025_Codebook llms Evaluating llms as measurement tools for political science concepts.pdf}
}

@article{Harris1954,
  title = {Distributional Structure},
  author = {Harris, Zellig S.},
  date = {1954-08},
  journaltitle = {Word},
  shortjournal = {\mkbibemph{WORD}},
  volume = {10},
  number = {2--3},
  pages = {146--162},
  issn = {0043-7956, 2373-5112},
  doi = {10.1080/00437956.1954.11659520},
  urldate = {2026-02-09},
  langid = {english},
  file = {D:\zotero_system\storage\D4BERMIF\ Harris1954_Distributional structure.pdf}
}

@article{HeseltineVonHohenberg2024,
  title = {Large Language Models as a Substitute for Human Experts in Annotating Political Text},
  author = {Heseltine, Michael and family=Hohenberg, given=Bernhard Clemm, prefix=von, useprefix=true},
  date = {2024-01-01},
  journaltitle = {Research \& Politics},
  volume = {11},
  number = {1},
  pages = {20531680241236239},
  publisher = {SAGE Publications Ltd},
  issn = {2053-1680},
  doi = {10.1177/20531680241236239},
  urldate = {2026-02-09},
  abstract = {Large-scale text analysis has grown rapidly as a method in political science and beyond. To date, text-as-data methods rely on large volumes of human-annotated training examples, which place a premium on researcher resources. However, advances in large language models (LLMs) may make automated annotation increasingly viable. This paper tests the performance of GPT-4 across a range of scenarios relevant for analysis of political text. We compare GPT-4 coding with human expert coding of tweets and news articles across four variables (whether text is political, its negativity, its sentiment, and its ideology) and across four countries (the United States, Chile, Germany, and Italy). GPT-4 coding is highly accurate, especially for shorter texts such as tweets, correctly classifying texts up to 95\% of the time. Performance drops for longer news articles, and very slightly for non-English text. We introduce a `hybrid' coding approach, in which disagreements of multiple GPT-4 runs are adjudicated by a human expert, which boosts accuracy. Finally, we explore downstream effects, finding that transformer models trained on hand-coded or GPT-4-coded data yield almost identical outcomes. Our results suggest that LLM-assisted coding is a viable and cost-efficient approach, although consideration should be given to task complexity.},
  langid = {english},
  file = {D:\zotero_system\storage\XKF44MNW\ Heseltinevon Hohenberg2024_Large language models as a substitute for human experts in annotating political text.pdf}
}

@article{HouleKayser2019,
  title = {The Two-Step Model of Clustered Democratization},
  author = {Houle, Christian and Kayser, Mark A.},
  date = {2019-11-01},
  journaltitle = {Journal of Conflict Resolution},
  volume = {63},
  number = {10},
  pages = {2421--2437},
  publisher = {SAGE Publications Inc},
  issn = {0022-0027},
  doi = {10.1177/0022002719875565},
  urldate = {2026-01-30},
  abstract = {Does democratization diffuse? For over two decades, numerous studies have asserted that democratization diffuses across countries but recent research has challenged this claim. Most recently, work by Brancati and Lucardi has buttressed this null finding by demonstrating that an oft assumed mechanism for the diffusion of democratization---the diffusion of pro-democracy protests---lacks empirical support. We review this contribution in the context of recent research and pose the question: if democratization does not diffuse, then why does democratization cluster in time and space? The answer, we argue, is that democratization occurs in two steps. First, common shocks, economic or political, lead to regime collapse. Then, diffusion does emerge in a second step: new elites are more likely to install a democracy following a regime collapse if neighboring countries have recently democratized. We present evidence from democratic transitions in 125 autocracies between 1875 and 2014.},
  langid = {english}
}

@book{Hu2026,
  title = {From Mouth to Mind: How China's Language Regime Shapes Mass Political Psychology},
  author = {Hu, Yue},
  date = {2026},
  publisher = {Springer},
  pubstate = {forthcoming}
}

@article{HuPizzi2022,
  title = {Breaking through the Language Barrier: The Role of Language Policy in Migration Decisions},
  shorttitle = {Breaking through the Language Barrier},
  author = {Hu, Yue and Pizzi, Elise},
  date = {2022},
  journaltitle = {China: An International Journal},
  volume = {20},
  number = {3},
  pages = {23--51},
  publisher = {NUS Press Pte Ltd},
  issn = {0219-8614},
  doi = {10.1353/chn.2022.0022},
  urldate = {2022-09-06},
  abstract = {Why do migrants choose some destinations over others? This study explores government language policy and the role of language differences in migration decisions. While governments can use language policy to reduce communication barriers, vernaculars remain critical in terms of regional communication and identity recognition. Using a survey experiment and econometric analyses of nationally representative data from China, the authors examine the communication and cultural roles of language in migration decisions in the context of a unifying common national language. The empirical tests find that migrants prefer destinations where local vernacular languages are more similar to their own, despite a common national language. Once migrants have moved, a common national language helps migrants communicate and therefore remain in their migration destinations, but it does not eliminate the role of the local vernacular as a cultural marker. The result is that both the common language and local vernacular matter for domestic migration, even in a country where the government has promoted a single common language for six decades. The findings have far-reaching implications for migration policy and citizenship management in general.},
  keywords = {language},
  file = {D:\zotero_system\storage\NWEX6XPJ\Hu_Pizzi2022_Breaking through the language barrier.pdf}
}

@article{HuSolt2025,
  title = {Macrointerest across Countries},
  author = {Hu, Yue and Solt, Frederick},
  date = {2025-04-28},
  journaltitle = {British Journal of Political Science},
  volume = {55},
  pages = {e71},
  issn = {0007-1234, 1469-2112},
  doi = {10.1017/S0007123424001042},
  urldate = {2025-05-06},
  abstract = {The extent to which the public takes an interest in politics has long been argued to be foundational to democracy, but the want of appropriate data has prevented cross-national and longitudinal analysis. This letter takes advantage of recent advances in latent-variable modelling of aggregate survey responses and a comprehensive collection of survey data to generate dynamic comparative estimates of macrointerest, that is, aggregate political interest, for over a hundred countries over the past four decades. These macrointerest scores are validated with other aggregate measures of political interest and of other types of political engagement. A cross-national and longitudinal analysis of macrointerest in advanced democracies reveals that along with election campaigns and inclusive institutions, it is good economic conditions, not bad times, that spur publics to greater interest in politics.},
  langid = {english},
  keywords = {method},
  file = {D:\zotero_system\storage\8M8LDRA3\ HuSolt2025_Macrointerest across countries.pdf}
}

@online{IBM2021,
  title = {What Is Machine Learning (ML)?},
  shorttitle = {What Is Machine Learning (ML)?},
  author = {{IBM}},
  date = {2021-09-22T00:00:00.000},
  urldate = {2024-08-21},
  abstract = {Machine learning (ML) is a branch of AI and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn.},
  langid = {american},
  organization = {Think: Tech news, education and events}
}

@report{IDCFutureScape2024,
  title = {2024年中国数据和分析市场十大预测},
  author = {{IDC FutureScape}},
  date = {2024-01-24},
  institution = {IDC Media Center},
  urldate = {2024-06-17},
  abstract = {IDC examines consumer markets by devices, applications, networks, and services to provide complete solutions for succeeding in these expanding markets.},
  file = {D:\zotero_system\storage\UVLB4R2G\getdoc.html}
}

@book{JamesEtAl2017a,
  ids = {James2013},
  title = {An Introduction to Statistical Learning: With Applications in R},
  shorttitle = {An Introduction to Statistical Learning},
  author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  date = {2017-09},
  edition = {1st ed. 2013, Corr. 7th printing 2017 edition},
  publisher = {Springer},
  location = {New York},
  abstract = {An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.},
  isbn = {978-1-4614-7137-0},
  timestamp = {2019-09-13T01:23:09Z}
}

@report{Kemp2024,
  title = {Digital 2024: Global Overview Report},
  author = {Kemp, Simon},
  date = {2024},
  institution = {Meltwater},
  urldate = {2024-06-17},
  file = {D:\zotero_system\storage\NITWKFVL\ 2024年全球数字化营销洞察报告-50亿社交媒体用户_Meltwater融文_2024.pdf.pdf}
}

@unpublished{King2015,
  type = {Guest talk},
  title = {Big Data Is Not about the Data!},
  author = {King, Gary},
  date = {2015-11-11},
  urldate = {2024-06-17},
  eventtitle = {Talk at the Capital Markets Cooperative Research Centre},
  langid = {english},
  venue = {Sydney, Australia},
  file = {D:\zotero_system\storage\DLKB4RWZ\ KingBig Data is Not About the Data!.pdf}
}

@incollection{King2016,
  title = {Preface: Big Data Is Not about the Data!},
  booktitle = {Computational Social Science: Discovery and Prediction},
  author = {King, Gary},
  editor = {Alvarez, R. Michael},
  date = {2016},
  publisher = {Cambridge University Press},
  location = {Cambridge},
  langid = {english},
  file = {D:\zotero_system\storage\ KingAlvarez2016_Preface big data is not about the data!.pdf}
}

@article{KingEtAl2017,
  ids = {KingEtAl2017a},
  title = {Computer-Assisted Keyword and Document Set Discovery from Unstructured Text},
  author = {King, Gary and Lam, Patrick and Roberts, Margaret E.},
  date = {2017},
  journaltitle = {American Journal of Political Science},
  volume = {61},
  number = {4},
  pages = {971--988},
  issn = {1540-5907},
  doi = {10.1111/ajps.12291},
  urldate = {2023-05-03},
  abstract = {The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Most seem to think that keyword selection is easy, since they do Google searches every day, but we demonstrate that humans perform exceedingly poorly at this basic task. We offer a better approach, one that also can help with following conversations where participants rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look-alike modeling; industry and intelligence analysis; and sentiment and topic analysis. We develop a computer-assisted (as opposed to fully automated or human-only) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with easy-to-understand Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon bombings, Chinese social media posts designed to evade censorship, and others.},
  issue = {4},
  langid = {english},
  keywords = {Keyword},
  file = {D:\Nutstore\01_Literature\King et al2017_Computer-Assisted Keyword and Document Set Discovery from Unstructured Text3.pdf}
}

@article{KrasseltDreesen2024,
  title = {Topic models indicate textual aboutness and pragmatics: Valuation practices in Islamophobic discourse},
  shorttitle = {Topic models indicate textual aboutness and pragmatics},
  author = {Krasselt, Julia and Dreesen, Philipp},
  date = {2024-03-22},
  journaltitle = {Journal of Cultural Analytics},
  volume = {7},
  number = {2},
  publisher = {Center for Digital Humanities, Princeton University},
  issn = {2371-4549},
  doi = {10.22148/001c.92535},
  urldate = {2026-03-27},
  abstract = {This study investigates the analysis of evaluative linguistic patterns within the context of the German-language Islamophobic online blog PI-News. The corpus consists of texts that refer to the Quran. The innovation of this research lies in two primary dimensions: Firstly, it adopts John Dewey's pragmatism to establish an explicit valuation theory. Secondly, it extends the boundaries of topic modeling in the digital humanities by integrating van Dijk's differentiation between aboutness and pragmatic macrostructures of texts. Notably, within these pragmatic macrostructures, the study highlights the practice of quoting Quran suras as a significant linguistic pattern. Together with the analysis of topic distribution and topic pathways in texts, the paper illustrates how evaluative practices reinforce the ideal of an Islam-free Europe.},
  langid = {None},
  file = {D:\zotero_system\storage\S9PG369I\ KrasseltDreesen2024_Topic models indicate textual aboutness and pragmatics Valuation practices in Islamophobic discours.pdf}
}

@article{Lasswell1927,
  title = {The Theory of Political Propaganda},
  author = {Lasswell, Harold D.},
  date = {1927},
  journaltitle = {American Political Science Review},
  volume = {21},
  number = {03},
  pages = {627--631},
  publisher = {Cambridge Univ Press},
  timestamp = {2019-10-29T01:14:50Z}
}

@book{Lasswell1927a,
  title = {Propaganda Technique in the World War},
  author = {Lasswell, Harold Dwight},
  date = {1927},
  eprint = {ysWYQgAACAAJ},
  eprinttype = {googlebooks},
  publisher = {K. Paul, Trench, Trubner \& Company, Limited},
  isbn = {978-0-598-50983-3},
  langid = {english},
  pagetotal = {233}
}

@article{LaverEtAl2003,
  title = {Extracting Policy Positions from Political Texts Using Words as Data},
  author = {Laver, Michael and Benoit, Kenneth and Garry, John},
  date = {2003-05},
  journaltitle = {American Political Science Review},
  volume = {97},
  number = {2},
  pages = {311--331},
  publisher = {Cambridge University Press},
  issn = {1537-5943, 0003-0554},
  doi = {10.1017/S0003055403000698},
  urldate = {2023-06-09},
  abstract = {We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the policy positions of political parties in Britain and Ireland, on both economic and social policy dimensions. We ``export'' the method to a non-English-language environment, analyzing the policy positions of German parties, including the PDS as it entered the former West German party system. Finally, we extend its application beyond the analysis of party manifestos, to the estimation of political positions from legislative speeches. Our ``language-blind'' word scoring technique successfully replicates published policy estimates without the substantial costs of time and labor that these require. Furthermore, unlike in any previous method for extracting policy positions from political texts, we provide uncertainty measures for our estimates, allowing analysts to make informed judgments of the extent to which differences between two estimated policy positions can be viewed as significant or merely as products of measurement error.We thank Raj Chari, Gary King, Michael McDonald, Gail McElroy, and three anonymous reviewers for comments on drafts of this paper.},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Laver et al2003_Extracting Policy Positions from Political Texts Using Words as Data.pdf}
}

@article{Liu2022,
  title = {Pronoun Usage as a Measure of Power Personalization: A General Theory with Evidence from the Chinese-Speaking World},
  shorttitle = {Pronoun Usage as a Measure of Power Personalization},
  author = {Liu, Amy H.},
  date = {2022-07},
  journaltitle = {British Journal of Political Science},
  volume = {52},
  number = {3},
  pages = {1258--1275},
  publisher = {Cambridge University Press},
  issn = {0007-1234, 1469-2112},
  doi = {10.1017/S0007123421000181},
  urldate = {2022-07-06},
  abstract = {How can the growing personalization of power be identified and measured ex ante? Extant measures in the authoritarian literature have traditionally focused on institutional constraints and more recently on individual behaviour -- such as purging opposition members from (and packing allies into) government bodies. This article offers a different strategy that examines leaders' individual rhetoric. It focuses on patterns of pronoun usage for the first person. The author argues that as leaders personalize power, they are less likely to use `I' (a pronoun linked to credit claiming and blame minimizing) and more likely to use `we' (the leader speaks for -- or with -- the populace). To test this argument, the study focuses on all major, scheduled speeches by all chief executives in the entire Chinese-speaking world -- that is, China, Singapore and Taiwan -- since independence. It finds a robust pattern between first-person pronouns and political constraints. To ensure the results are not driven by the Chinese sample, the rhetoric of four other political leaders is considered: Albania's Hoxha, North Korea's Kim Il Sung, Hungary's Orb\'an and Ecuador's Correa. The implications of this project suggest that how leaders talk can provide insights into how they perceive their rule.},
  issue = {3},
  langid = {english},
  keywords = {Albania,Chinese,Ecuador,Hungary,North Korea,personalism,pronouns},
  file = {D:\zotero_system\storage\SGWK626T\Liu2022_Pronoun Usage as a Measure of Power Personalization.pdf}
}

@article{Lowe2017,
  title = {Understanding wordscores},
  author = {Lowe, Will},
  date = {2017-01-04},
  journaltitle = {Political Analysis},
  volume = {16},
  number = {4},
  pages = {356--371},
  publisher = {Cambridge University Press},
  issn = {1047-1987, 1476-4989},
  doi = {10.1093/pan/mpn004},
  urldate = {2023-06-09},
  abstract = {Wordscores is a widely used procedure for inferring policy positions, or scores, for new documents on the basis of scores for words derived from documents with known scores. It is computationally straightforward, requires no distributional assumptions, but has unresolved practical and theoretical problems. In applications, estimated document scores are on the wrong scale and the theoretical development does not specify a statistical model, so it is unclear what assumptions the method makes about political text and how to tell whether they fit particular text analysis applications. The first part of the paper demonstrates that badly scaled document score estimates reflect deeper problems with the method. The second part shows how to understand Wordscores as an approximation to correspondence analysis which itself approximates a statistical ideal point model for words. Problems with the method are identified with the conditions under which these layers of approximation fail to ensure consistent and unbiased estimation of the parameters of the ideal point model.},
  langid = {afrikaans}
}

@inproceedings{LuEtAl2011,
  title = {Multi-Aspect Sentiment Analysis with Topic Models},
  booktitle = {2011 IEEE 11th International Conference on Data Mining Workshops},
  author = {Lu, Bin and Ott, Myle and Cardie, Claire and Tsou, Benjamin K.},
  date = {2011-12},
  pages = {81--88},
  issn = {2375-9259},
  doi = {10.1109/ICDMW.2011.125},
  abstract = {We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge - in the form of seed words - to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that overall ratings can be used in conjunction with our sentence labelings to achieve reasonable performance compared to a fully supervised baseline. When gold-standard aspect-ratings are available, we find that topic model based features can be used to improve unsophisticated supervised baseline performance, in agreement with previous multi-aspect rating prediction work. This improvement is diminished, however, when topic model features are paired with a more competitive supervised baseline - a finding not acknowledged in previous work.},
  eventtitle = {2011 IEEE 11th International Conference on Data Mining Workshops},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Lu et al2011_Multi-aspect Sentiment Analysis with Topic Models.pdf}
}

@book{Matloff2020,
  title = {Probability and Statistics for Data Science: Math + R + Data},
  shorttitle = {Probability and Statistics for Data Science},
  author = {Matloff, Norman},
  date = {2020},
  publisher = {{Chapman and Hall/CRC}},
  location = {Boca Raton London New York},
  abstract = {Probability and Statistics for Data Science: Math + R + Data covers "math stat"\texthorizontalbar distributions, expected value, estimation etc.\texthorizontalbar but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.Prerequisites are calculus, some matrix algebra, and some experience in programming.Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.},
  isbn = {978-1-138-39329-5},
  langid = {english},
  pagetotal = {412}
}

@online{McCabe2023,
  title = {8 of the Biggest Excel Mistakes of All Time},
  author = {McCabe, Ashleigh},
  date = {2023-01-30},
  urldate = {2025-07-07},
  abstract = {Excel is great for data analysis however it can be easy to make grave errors, leading to bad business decisions. Here are the biggest excel mistakes ever},
  langid = {american},
  organization = {The marketing slice by Hurree},
  file = {D:\zotero_system\storage\EU2J6LVQ\8-of-the-biggest-excel-mistakes-of-all-time.html}
}

@book{NationalHealthCommission2019,
  title = {Report on china's migrant population development 2018},
  author = {{National Health Commission}},
  date = {2019},
  publisher = {China Population Publishing House},
  location = {Beijing},
  langid = {cn},
  timestamp = {2020-09-11T13:25:19Z}
}

@article{OrnsteinEtAl2025,
  title = {How to Train Your Stochastic Parrot: Large Language Models for Political Texts},
  shorttitle = {How to Train Your Stochastic Parrot},
  author = {Ornstein, Joseph T. and Blasingame, Elise N. and Truscott, Jake S.},
  date = {2025-04},
  journaltitle = {Political Science Research and Methods},
  volume = {13},
  number = {2},
  pages = {264--281},
  issn = {2049-8470, 2049-8489},
  doi = {10.1017/psrm.2024.64},
  urldate = {2026-02-09},
  abstract = {We demonstrate how few-shot prompts to large language models (LLMs) can be effectively applied to a wide range of text-as-data tasks in political science---including sentiment analysis, document scaling, and topic modeling. In a series of pre-registered analyses, this approach outperforms conventional supervised learning methods without the need for extensive data pre-processing or large sets of labeled training data. Performance is comparable to expert and crowd-coding methods at a fraction of the cost. We propose a set of best practices for adapting these models to social science measurement tasks, and develop an open-source software package for researchers.},
  langid = {english},
  file = {D:\zotero_system\storage\JQI2JVW9\ OrnsteinEtAl 2025_How to train your stochastic parrot large language models for political texts.pdf}
}

@article{Pennebaker2017,
  title = {Mind Mapping: Using Everyday Language to Explore Social \& Psychological Processes},
  shorttitle = {Mind Mapping},
  author = {Pennebaker, James W.},
  date = {2017-01-01},
  journaltitle = {Procedia Computer Science},
  shortjournal = {Procedia Computer Science},
  series = {Data Analytics Summit II; Structuring the UNSTRUCTURED: The Missing Element of Analytics, 14-16 December 2015, Harrisburg, USA},
  volume = {118},
  pages = {100--107},
  issn = {1877-0509},
  doi = {10.1016/j.procs.2017.11.150},
  urldate = {2023-04-23},
  abstract = {The ways we express ourselves in writing and speaking reveal who we are. Historically, most psychologists, social media experts, and even computer scientists have focused more on what people were saying rather than how they were saying what they were saying. Language content is, of course, critical to basic communication. Equally interesting is an analysis of common words associated with speaking style --- words such as pronouns, prepositions, articles, and other function words. An increasing number of studies have found that function words (also thought of as stop words) provide clues to deception, status, intelligence, emotional state, the quality of social relationships, and personality. In addition to summarizing recent research on the social dynamics of language, the talk will point to the natural alliance of computer science and the field of social and personality psychology..},
  langid = {english},
  keywords = {expressive writing,function words,language,liwc,social media,text analysis},
  file = {D:\Nutstore\01_Literature\Pennebaker2017_Mind mapping.pdf}
}

@article{PennebakerGraybeal2001,
  title = {Patterns of Natural Language Use: Disclosure, Personality, and Social Integration},
  shorttitle = {Patterns of Natural Language Use},
  author = {Pennebaker, James W. and Graybeal, Anna},
  date = {2001-06},
  journaltitle = {Current Directions in Psychological Science},
  shortjournal = {Curr Dir Psychol Sci},
  volume = {10},
  number = {3},
  pages = {90--93},
  issn = {0963-7214, 1467-8721},
  doi = {10.1111/1467-8721.00123},
  urldate = {2023-05-13},
  abstract = {When people write about their deepest thoughts and feelings about an emotionally significant event, numerous benefits in many domains (e.g., health, achievement, and well-being) result. As one step in understanding how writing achieves these effects, we have developed a computer program that provides a ``fingerprint'' of the words people use in writing or in natural settings. Analyses of text samples indicate that particular patterns of word use predict health and also reflect personality styles. We have also discovered that language use in the laboratory writing paradigm is associated with changes in social interactions and language use in the real world. The implications for using computer-based text analysis programs in the development of psychological theory are discussed.},
  issue = {3},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Pennebaker_Graybeal2001_Patterns of Natural Language Use.pdf}
}

@inproceedings{PenningtonEtAl2014,
  title = {GloVe: Global Vectors for Word Representation},
  shorttitle = {GloVe},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher},
  date = {2014-10},
  pages = {1532--1543},
  publisher = {Association for Computational Linguistics},
  location = {Doha, Qatar},
  doi = {10.3115/v1/D14-1162},
  urldate = {2023-05-27},
  eventtitle = {EMNLP 2014},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Pennington et al2014_GloVe.pdf}
}

@article{Ponweiser2012,
  title = {Latent dirichlet allocation in R},
  author = {Ponweiser, Martin},
  date = {2012-05-01},
  journaltitle = {Latent Dirichlet Allocation in R},
  series = {Theses / Institute for Statistics and Mathematics},
  publisher = {{WU Vienna University of Economics and Business}},
  location = {Vienna},
  abstract = {Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper "Finding scientific topics" by Thomas Griffiths and Mark Steyvers within the framework of the R statistical programming language and the R\textasciitilde package topicmodels by Bettina Gr\"un and Kurt Hornik. The complete process, including extraction of a text corpus from the PNAS journal's website, data preprocessing, transformation into a document-term matrix, model selection, model estimation, as well as presentation of the results, is fully documented and commented. The outcome closely matches the analyses of the original paper, therefore the research by Griffiths/Steyvers can be reproduced. Furthermore, this thesis proves the suitability of the R environment for text mining with LDA.},
  langid = {ngerman}
}

@report{RadicatiTeam2024,
  title = {Email Statistics Report：2024-2028},
  author = {{Radicati Team}},
  date = {2024-12-09},
  institution = {The Radicati Group, Inc.},
  urldate = {2026-01-12},
  file = {D:\zotero_system\storage\LCX4U79K\ Email-Statistics-Report-2024-2028-Executive-Summary.pdf}
}

@article{RectorEtAl2024,
  title = {Does Eco-Certification Change Public Opinion of Salmon Aquaculture in Canada? A Comparison of Communities with and without Salmon Farms},
  shorttitle = {Does Eco-Certification Change Public Opinion of Salmon Aquaculture in Canada?},
  author = {Rector, Megan E. and Filgueira, Ramon and Grant, Jon},
  date = {2024-01-02},
  journaltitle = {Aquaculture Economics \& Management},
  volume = {28},
  number = {1},
  pages = {32--55},
  publisher = {Taylor \& Francis},
  issn = {1365-7305},
  doi = {10.1080/13657305.2023.2196948},
  urldate = {2026-01-30},
  abstract = {Aquaculture eco-certification is associated with some producer-level benefits including price premiums and market access; however, reputational benefits from eco-certification are unclear. A public survey was used to understand the effect of eco-certification on opinion of salmon farming in two Canadian provinces (British Columbia and Nova Scotia) and differences between communities where farms are located (communities of place) and communities geographically distant from farms (communities of interest). Eco-certification had an overall positive effect on opinion, especially amongst people with a negative opinion of salmon farming who value far-reaching social outcomes of farming. Communities of interest had a more negative opinion of salmon farming and eco-certified salmon farming and were more concerned about local environmental impacts than communities of place while communities of place valued economic outcomes more than communities of interest. The role of eco-certification in public acceptance of aquaculture is limited by a lack of trust in eco-certification and failure to address local issues including conflict amongst marine users.}
}

@online{Riesch2025,
  title = {Wordkrill: Extending Wordfish into the Multidimensional Political Space},
  shorttitle = {Wordkrill},
  author = {Riesch, Benjamin},
  date = {2025-07-02},
  eprint = {2506.20275},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.2506.20275},
  urldate = {2026-03-26},
  abstract = {Spatial models are central to the study of political conflict, yet their empirical application often depends on text-based methods. A prominent example is the Wordfish model, which estimates actor positions from political texts. However, a key limitation of Wordfish is its unidimensionality, despite the well-established multidimensional nature of political competition. This contribution introduces Wordkrill, a multidimensional extension of Wordfish that retains the original model's interpretability while allowing for efficient estimation of political positions along multiple latent dimensions. After presenting the mathematical framework of Wordkrill, its utility through brief applications to party manifestos and parliamentary speeches is demonstrated. These examples illustrate both the practical advantages and current limitations of the approach.},
  pubstate = {prepublished},
  file = {D\:\\zotero_system\\storage\\MSYL5UDU\\ Riesch2025_Wordkrill Extending Wordfish into the multidimensional political space.pdf;D\:\\zotero_system\\storage\\QADK64QC\\2506.html}
}

@book{RitterMedhurst2004,
  title = {Presidential Speechwriting: From the New Deal to the Reagan Revolution and Beyond},
  shorttitle = {Presidential Speechwriting},
  author = {Ritter, Kurt and Medhurst, Martin J.},
  date = {2004-03-15},
  eprint = {ncTc5ffnDg8C},
  eprinttype = {googlebooks},
  publisher = {Texas A\&M University Press},
  abstract = {The rise of the media presidency through radio and television broadcasts has heightened the visibility and importance of presidential speeches in determining the effectiveness and popularity of the President of the United States. Not surprisingly, this development has also witnessed the rise of professional speechwriters to craft the words the chief executive would address to the nation.Yet, as this volume of expert analyses graphically demonstrates, the reliance of individual presidents on their speechwriters has varied with the rhetorical skill of the officeholder himself, his managerial style, and his personal attitude toward public speaking. The individual chapters here (two by former White House speechwriters) give fascinating insight into the process and development of presidential speechwriting from Franklin D. Roosevelt's administration to Ronald Reagan's. Some contributors, such as Charles Griffin writing on Eisenhower and Moya Ball on Johnson, offer case studies of specific speeches to gain insight into those presidents. Other chapters focus on institutional arrangements and personal relationships, rhetorical themes characterizing an administration, or the relationship between words and policies to shed light on presidential speechwriting.The range of presidents covered affords opportunities to examine various factors that make rhetoric successful or not, to study alternative organizational arrangements for speechwriters, and even to consider the evolution of the rhetorical presidency itself. Yet, the volume's single focus on speechwriting and the analytic overviews provided by Martin J. Medhurst not only bring coherence to the work, but also make this book an exemplar of how unity can be achieved from a diversity of approaches. Medhurst's introduction of ten ``myths'' in the scholarship on presidential speeches and his summary of the enduring issues in the practice of speechwriting pull together the work of individual contributors. At the same time, his introduction and conclusion transcend particular presidents by providing generalizations on the role of speechwriting in the modern White House.},
  isbn = {978-1-58544-392-5},
  langid = {english},
  pagetotal = {252}
}

@article{RobertsEtAl2013,
  title = {The Structural Topic Model and Applied Social Science},
  author = {Roberts, Margaret E. and Stewart, Brandon M. and Tingley, Dustin and Airoldi, Edoardo M.},
  date = {2013},
  journaltitle = {Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation},
  volume = {1737},
  pages = {1--4},
  owner = {Sammo},
  timestamp = {2019-10-29T01:14:50Z}
}

@article{RobertsEtAl2014,
  title = {Stm: R Package for Structural Topic Models},
  author = {Roberts, Margaret E. and Stewart, Brandon M. and Tingley, Dustin},
  date = {2014},
  journaltitle = {R Package},
  volume = {1},
  pages = {12},
  owner = {Sammo},
  timestamp = {2019-10-29T01:14:50Z}
}

@article{RobertsEtAl2014a,
  ids = {robertsₛtructural₂014,robertsₛtructural₂014-1},
  title = {Structural Topic Models for Open-Ended Survey Responses},
  author = {Roberts, Margaret E. and Stewart, Brandon M. and Tingley, Dustin and Lucas, Christopher and Leder-Luis, Jetson and Gadarian, Shana Kushner and Albertson, Bethany and Rand, David G.},
  date = {2014},
  journaltitle = {American Journal of Political Science},
  volume = {58},
  number = {4},
  pages = {1064--1082},
  publisher = {Wiley Online Library},
  issn = {1540-5907},
  doi = {10.1111/ajps.12103},
  urldate = {2025-02-15},
  abstract = {Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.},
  langid = {english},
  file = {D\:\\zotero_system\\storage\\C7NFKPDU\\ RobertsEtAl 2014_Structural topic models for open-ended survey responses.pdf;D\:\\zotero_system\\storage\\SILJS8T7\\ajps.html}
}

@article{RobertsEtAl2016a,
  title = {A Model of Text for Experimentation in the Social Sciences},
  author = {Roberts, Margaret E. and Stewart, Brandon M. and Airoldi, Edoardo M.},
  date = {2016-07-02},
  journaltitle = {Journal of the American Statistical Association},
  volume = {111},
  number = {515},
  pages = {988--1003},
  publisher = {Taylor \& Francis},
  issn = {0162-1459},
  doi = {10.1080/01621459.2016.1141684},
  urldate = {2023-05-17},
  abstract = {Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want to move beyond exploration, to measurement and experimentation, and make inference about social and political processes that drive discourse and content. In this article, we develop a model of text data that supports this type of substantive research. Our approach is to posit a hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates. In this model, topical prevalence and topical content are specified as a simple generalized linear model on an arbitrary number of document-level covariates, such as news source and time of release, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework. We demonstrate the proposed methodology by analyzing a collection of news reports about China, where we allow the prevalence of topics to evolve over time and vary across newswire services. Our methods quantify the effect of news wire source on both the frequency and nature of topic coverage. Supplementary materials for this article are available online.},
  langid = {english},
  file = {D:\zotero_system\storage\4Z9I6K3P\Roberts et al2016_A Model of Text for Experimentation in the Social Sciences.pdf}
}

@article{RobertsEtAl2020,
  title = {Adjusting for Confounding with Text Matching},
  author = {Roberts, Margaret E and Stewart, Brandon M and Nielsen, Richard A},
  date = {2020},
  journaltitle = {American Journal of Political Science},
  pages = {Forthcoming},
  abstract = {We identify situations in which conditioning on text can address confounding in observational studies. We argue that a matching approach is particularly well-suited to this task, but existing matching methods are ill-equipped to handle high-dimensional text data. Our proposed solution is to estimate a low-dimensional summary of the text covariates and condition on this summary via matching. We propose a method of text matching, Topical Inverse Regression Matching, that allows the analyst to match both on the topical content of confounding documents and the probability that each of these documents is treated. We validate and illustrate the importance of conditioning on text to address confounding with two applications: the effect of perceptions of author gender on citation counts in academia and the effects of censorship on Chinese social media users.},
  langid = {english},
  timestamp = {2020-03-27T06:40:58Z},
  file = {D:\zotero_system\storage\GK26D6SV\Robertset alAdjusting for Confounding with Text Matching.pdf}
}

@article{Rubin1996,
  title = {Multiple Imputation after 18+ Years},
  author = {Rubin, Donald B.},
  date = {1996-06-01},
  journaltitle = {Journal of the American Statistical Association},
  volume = {91},
  number = {434},
  pages = {473--489},
  publisher = {Taylor \& Francis},
  issn = {0162-1459},
  doi = {10.1080/01621459.1996.10476908},
  urldate = {2026-02-06},
  abstract = {Multiple imputation was designed to handle the problem of missing data in public-use data bases where the data-base constructor and the ultimate user are distinct entities. The objective is valid frequency inference for ultimate users who in general have access only to complete-data software and possess limited knowledge of specific reasons and models for nonresponse. For this situation and objective, I believe that multiple imputation by the data-base constructor is the method of choice. This article first provides a description of the assumed context and objectives, and second, reviews the multiple imputation framework and its standard results. These preliminary discussions are especially important because some recent commentaries on multiple imputation have reflected either misunderstandings of the practical objectives of multiple imputation or misunderstandings of fundamental theoretical results. Then, criticisms of multiple imputation are considered, and, finally, comparisons are made to alternative strategies.}
}


@article{SchroterDu2022,
  title = {Validating Topic Modeling as a Method of Analyzing Sujet and Theme},
  author = {Schr\"oter, Julian and Du, Keli},
  date = {2022-12-06},
  journaltitle = {Journal of Computational Literary Studies},
  volume = {1},
  number = {1},
  publisher = {Universit\"ats- und Landesbibliothek Darmstadt},
  issn = {2940-1348},
  doi = {10.48694/jcls.91},
  urldate = {2026-03-27},
  abstract = {In Computational Literary Studies (CLS), several procedures for thematic analysis have been adapted from NLP and Computer Science. Among these procedures, topic modeling is the most prominent and popular technique. We maintain, however, that this procedure is used only in the context of exploration up to date, but not in the context of justification. When we seek to prove assumptions concerning the correlation between genres, methods of computational text analysis have to be set up in research environments of justification, i.e. in environments of hypothesis testing. We provide a holistic model of validation and conceptual disambiguation of the notion of aboutness as sujet, fabula, and theme, and discuss essential methodological requirements for hypothesis-based analysis. As we maintain that validation has to be performed for individual tasks respectively, we shall perform empirical validation of topic modeling based on a new corpus of German novellas and comprehensive annotations and draw hypothetical generalizations on the applicability of topic modeling for analyzing aboutness in the domain of narrative fiction.},
  langid = {english},
  file = {D:\zotero_system\storage\GTHAL5IC\ SchröterDu2022_Validating Topic Modeling as a Method of Analyzing Sujet and Theme.pdf}
}

@article{SenguptaEtAl2023,
  title = {Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization},
  author = {Sengupta, Nandana and Udell, Madeleine and Srebro, Nathan and Evans, James},
  date = {2023-02-01},
  journaltitle = {Sociological Methodology},
  volume = {53},
  number = {1},
  pages = {72--114},
  publisher = {SAGE Publications Inc},
  issn = {0081-1750},
  doi = {10.1177/00811750221125799},
  urldate = {2026-03-28},
  abstract = {Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data reconstruction. This approach may enable social scientists to draw new conclusions from sparse data sets with a large number of features, for example, historical or archival sources, online surveys with high attrition rates, or data sets created from Web scraping, which confound traditional imputation techniques. The authors introduce matrix factorization techniques and detail their probabilistic interpretation, and they demonstrate these techniques' consistency with Rubin's multiple imputation framework. The authors show via simulations using artificial data and data from real-world subsets of the General Social Survey and National Longitudinal Study of Youth cases for which matrix factorization techniques may be preferred. These findings recommend the use of matrix factorization for data reconstruction in several settings, particularly when data are Boolean and categorical and when large proportions of the data are missing.},
  langid = {english}
}

@online{Silipo2020,
  title = {10 Common Issues Using Excel for Data Operations},
  author = {Silipo, Rosaria},
  date = {2020-05-04},
  urldate = {2025-07-07},
  abstract = {Here we look at the ten most common issues when working with an Excel spreadsheet to transform and analyze data.},
  langid = {english},
  organization = {KNIME},
  file = {D:\zotero_system\storage\MDZMRWFM\ten-common-issues-when-using-excel-for-data-operations.html}
}

@article{SlapinProksch2008,
  title = {A Scaling Model for Estimating Time-Series Party Positions from Texts},
  author = {Slapin, Jonathan B. and Proksch, Sven-Oliver},
  date = {2008},
  journaltitle = {American Journal of Political Science},
  volume = {52},
  number = {3},
  pages = {705--722},
  issn = {1540-5907},
  doi = {10.1111/j.1540-5907.2008.00338.x},
  urldate = {2026-03-26},
  abstract = {Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing text-based methods face challenges in producing valid and reliable time-series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time-series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.},
  langid = {english},
  file = {D\:\\Nutstore\\01_Literature\\Slapin_Proksch2008_A Scaling Model for Estimating Time-Series Party Positions from Texts.pdf;D\:\\zotero_system\\storage\\WZTXQPTX\\j.1540-5907.2008.00338.html}
}

@article{SoltEtAl2017,
  ids = {SoltEtAl2017a,SoltEtAl2017b},
  title = {Economic Inequality and Class Consciousness},
  author = {Solt, Frederick and Hu, Yue and Hudson, Kevan and Song, Jungmin and Yu, Dong "Erico"},
  date = {2017-07},
  journaltitle = {The Journal of Politics},
  volume = {79},
  number = {3},
  pages = {1079--1083},
  issn = {0022-3816, 1468-2508},
  doi = {10.1086/690971},
  copyright = {All rights reserved},
  issue = {3},
  langid = {english},
  keywords = {cognition},
  timestamp = {2021-02-02T03:00:25Z},
  file = {D\:\\zotero_system\\storage\\7LIZMBEA\\Solt et al2017_Economic inequality and class consciousness.pdf;D\:\\zotero_system\\storage\\9ZNWZSAJ\\ SoltEtAl 2017_Economic Inequality and Class Consciousness.pdf;D\:\\zotero_system\\storage\\RXDU4PMX\\ SoltEtAl 2017_Economic Inequality and Class Consciousness.pdf}
}

@article{StekhovenBuhlmann2012,
  title = {MissForest: Non-Parametric Missing Value Imputation for Mixed-Type Data},
  author = {Stekhoven, Daniel J. and B\"uhlmann, Peter},
  date = {2012-01},
  journaltitle = {Bioinformatics},
  volume = {28},
  number = {1},
  pages = {112--118},
  issn = {1367-4803},
  abstract = {Abstract. Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the},
  langid = {english},
  keywords = {missing value},
  timestamp = {2021-04-13T01:57:03Z}
}

@article{SuEtAl2020,
  title = {A Pattern Recognition Framework for Detecting Changes in Chinese Internet Management System},
  author = {Su, Yu-Sung and Ruan, Yanqin and Sun, Siyu and Chang, Yu-Tzung},
  date = {2020-09},
  journaltitle = {Journal of Social Computing},
  volume = {1},
  number = {1},
  pages = {28--39},
  issn = {2688-5255},
  doi = {10.23919/JSC.2020.0004},
  abstract = {Past studies on the Chinese Internet management system have revealed a smart Internet management system that takes advantage of time to filter content with collective action potential. How and why such a system was institutionalized? We offer a historical institutional analysis to explain the way in which the system evolved. We implement social network analysis to examine the Weibo posts of recurrent events, the elections in Area A in 2016 and 2018, to identify pattern changes in the system. There are two aspects of the changes: the centralization of the command line to a single authority and the implementation of a discriminatory strategy to deal with the various online expressions together forming this intelligent system. The improved Chinese information surveillance system demonstrates both a top-down information management and a bottom-up opinion formation.},
  keywords = {bottom-up opinion formation,Chinese information surveillance system,Chinese Internet management system,Cyberspace,devolution paradox,historical institutional analysis,Image color analysis,information management,intelligent system,internet,internet censorship,pattern changes,pattern recognition,pattern recognition framework,regulation,smart Internet management system,social network analysis,social networking (online),top-down information management},
  timestamp = {2020-12-12T00:31:08Z},
  file = {D:\zotero_system\storage\4ZDHV3WC\Suet al2020_A Pattern Recognition Framework for Detecting Changes in Chinese Internet.pdf}
}

@article{TaiEtAl2024,
  title = {Democracy, Public Support, and Measurement Uncertainty},
  author = {Tai, Yuehong `Cassandra' and Hu, Yue and Solt, Frederick},
  date = {2024-02},
  journaltitle = {American Political Science Review},
  volume = {118},
  number = {1},
  pages = {512--518},
  publisher = {Cambridge University Press},
  issn = {0003-0554, 1537-5943},
  doi = {10.1017/S0003055422000429},
  urldate = {2024-02-27},
  abstract = {Do democratic regimes depend on public support to avoid backsliding? Does public support, in turn, respond thermostatically to changes in democracy? Two prominent recent studies (Claassen 2020a; 2020b) reinvigorated the classic hypothesis on the positive relationship between public support for democracy and regime survival---and challenged its reciprocal counterpart---by using a latent variable approach to measure mass democratic support from cross-national survey data. However, both studies used only the point estimates of democratic support. We show that incorporating the concomitant measurement uncertainty into these analyses reveals that there is no support for either study's conclusion. Efforts to minimize the uncertainty by incorporating additional survey data still fail to yield evidence in support of either hypothesis. These results underscore the need for both more nuanced analyses of the relationships between public support and democracy and taking measurement uncertainty into account when working with latent variables.},
  langid = {english},
  keywords = {cognition},
  file = {D\:\\zotero_system\\storage\\7MMM87VK\\Tai et al2024_Democracy, Public Support, and Measurement Uncertainty.pdf;D\:\\zotero_system\\storage\\GEDA4WNZ\\Tai et al2022_Democracy, Public Support, and Measurement Uncertainty.pdf}
}

@misc{TheTidyverseTeam2025,
  title = {Tidyverse Style Guide},
  author = {{The tidyverse team}},
  date = {2025-03-01},
  urldate = {2025-05-18},
  langid = {english},
  file = {D:\zotero_system\storage\NXFYYH6P\style.tidyverse.org.html}
}

@article{Todaro,
  title = {A Model of Labor Migration and Urban Unemployment in Less Developed Countries},
  author = {Todaro, Michael P},
  pages = {12},
  langid = {english},
  timestamp = {2019-04-20T02:12:25Z}
}

@inproceedings{VaswaniEtAl2017,
  title = {Attention Is All You Need},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
  date = {2017},
  volume = {30},
  publisher = {Curran Associates, Inc.},
  urldate = {2024-01-07},
  abstract = {The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms.  We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.},
  langid = {english},
  file = {D:\zotero_system\storage\HPJE38BB\Vaswani et al2017_Attention is All you Need.pdf}
}

@article{VilhelmsdottirKristinsson2018,
  title = {Political Trust in Iceland: Performance or Politics?},
  shorttitle = {Political Trust in Iceland},
  author = {Vilhelmsd\'ottir, Sj\"ofn and Kristinsson, Gunnar Helgi},
  date = {2018-05-31},
  eprint = {20.500.11815/787},
  eprinttype = {hdl},
  publisher = {Stofnun stj\'orns\'yslufr\ae\dh a og stj\'ornm\'ala vi\dh{} H\'ask\'ola \'Islands},
  issn = {1670-6803},
  urldate = {2026-01-30},
  abstract = {Economic performance has a well-known relationship to political trust. If the economy is perceived as performing well, the levels of political trust are likely to improve. During the 2008 economic crash in Iceland, this relationship seemed vindicated once more. Political trust in Iceland fell from very high to medium levels. Paradoxically, strong indicators of good economic performance after the crash have not been reflected in similarly strong indicators of greater political trust. Thus, while the economy has recovered, political trust has not followed suit to a comparable degree. To account for this discrepancy, we analyse the data on political trust in Iceland before and after the crash. Our findings indicate that while the economy is important in generating political trust, improved economic performance does not account for the whole scenario. The political impact of the crisis remains an obstacle to re-establishing political trust to previous levels, as identifying with a party in the government fails to contribute to political trust in the previous manner. Similarly, personal experience of the crisis may have created a sense of alienation among age cohorts that were particularly affected by the crash and the subsequent economic recession.},
  langid = {english},
  file = {D:\zotero_system\storage\VZGMFIL8\ VilhelmsdóttirKristinsson2018_Political trust in Iceland Performance or politics.pdf}
}

@article{VonHippel2020,
  title = {How Many Imputations Do You Need? A Two-Stage Calculation Using a Quadratic Rule},
  shorttitle = {How Many Imputations Do You Need?},
  author = {family=Hippel, given=Paul T., prefix=von, useprefix=true},
  date = {2020-08-01},
  journaltitle = {Sociological Methods \& Research},
  volume = {49},
  number = {3},
  pages = {699--718},
  publisher = {SAGE Publications Inc},
  issn = {0049-1241},
  doi = {10.1177/0049124117747303},
  urldate = {2026-02-06},
  abstract = {When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2--10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would not change (much) if you imputed the data again. For replicable SE estimates, the required number of imputations increases quadratically with the fraction of missing information (not linearly, as previous studies have suggested). I recommend a two-stage procedure in which you conduct a pilot analysis using a small-to-moderate number of imputations, then use the results to calculate the number of imputations that are needed for a final analysis whose SE estimates will have the desired level of replicability. I implement the two-stage procedure using a new SAS macro called \%mi\_combine and a new Stata command called how\_many\_imputations.},
  langid = {english},
  keywords = {missing,multiple imputation},
  file = {D:\zotero_system\storage\Y5F2HURF\ von Hippel2020_How many imputations do you need A two-stage calculation using a quadratic rule.pdf}
}

@article{Walker1969,
  title = {The Diffusion of Innovations among the American States},
  author = {Walker, Jack L.},
  date = {1969},
  journaltitle = {The American Political Science Review},
  volume = {63},
  number = {3},
  pages = {880--899},
  issn = {0003-0554},
  doi = {10.2307/1954434},
  timestamp = {2019-12-31T01:30:45Z}
}

@online{WangEtAl2024a,
  title = {Selecting between BERT and GPT for Text Classification in Political Science Research},
  author = {Wang, Yu and Qu, Wen and Ye, Xin},
  date = {2024-11-07},
  eprint = {2411.05050},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2411.05050},
  urldate = {2026-02-09},
  abstract = {Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across various classification tasks, differing in the number of classes and complexity, to evaluate the effectiveness of BERT-based versus GPT-based models in low-data scenarios. Our findings indicate that while zero-shot and few-shot learning with GPT models provide reasonable performance and are well-suited for early-stage research exploration, they generally fall short - or, at best, match - the performance of BERT fine-tuning, particularly as the training set reaches a substantial size (e.g., 1,000 samples). We conclude by comparing these approaches in terms of performance, ease of use, and cost, providing practical guidance for researchers facing data limitations. Our results are particularly relevant for those engaged in quantitative text analysis in low-resource settings or with limited labeled data.},
  pubstate = {prepublished},
  file = {D:\zotero_system\storage\TLQKFML7\ WangEtAl 2024_Selecting between BERT and GPT for text classification in political science research.pdf}
}

@article{Watanabe2018,
  title = {Newsmap: a semi-supervised approach to geographical news classification},
  author = {Watanabe, Kohei},
  date = {2018},
  journaltitle = {Digital Journalism},
  volume = {6},
  number = {4},
  pages = {294--309},
  doi = {10.1080/21670811.2017.1293487},
  urldate = {2023-06-07},
  abstract = {This paper presents the results of an evaluation of three different types of geographical news classification methods: (1) simple keyword matching, a popular method in media and communications rese...},
  langid = {indonesian}
}

@article{Watanabe2021,
  title = {Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages},
  shorttitle = {Latent Semantic Scaling},
  author = {Watanabe, Kohei},
  date = {2021-04-03},
  journaltitle = {Communication Methods and Measures},
  volume = {15},
  number = {2},
  pages = {81--102},
  publisher = {Routledge},
  issn = {1931-2458},
  doi = {10.1080/19312458.2020.1832976},
  urldate = {2023-06-08},
  abstract = {Many social scientists recognize that quantitative text analysis is a useful research methodology, but its application is still concentrated in documents written in European languages, especially English, and few sub-fields of political science, such as comparative politics and legislative studies. This seems to be due to the absence of flexible and cost-efficient methods that can be used to analyze documents in different domains and languages. Aiming to solve this problem, this paper proposes a semisupervised document scaling technique, called Latent Semantic Scaling (LSS), which can locate documents on various pre-defined dimensions. LSS achieves this by combining user-provided seed words and latent semantic analysis (word embedding). The article demonstrates its flexibility and efficiency in large-scale sentiment analysis of New York Times articles on the economy and Asahi Shimbun articles on politics. These examples show that LSS can produce results comparable to that of the Lexicoder Sentiment Dictionary (LSD) in both English and Japanese with only small sets of sentiment seed words. A new heuristic method that assists LSS users to choose a near-optimal number of singular values to obtain word vectors that best capture differences between documents on target dimensions is also presented.},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Watanabe2021_Latent Semantic Scaling.pdf}
}

@article{WatanabeBaturo2024,
  title = {Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences},
  shorttitle = {Seeded Sequential LDA},
  author = {Watanabe, Kohei and Baturo, Alexander},
  date = {2024-02-01},
  journaltitle = {Social Science Computer Review},
  volume = {42},
  number = {1},
  pages = {224--248},
  publisher = {SAGE Publications Inc},
  issn = {0894-4393},
  doi = {10.1177/08944393231178605},
  urldate = {2026-03-28},
  abstract = {Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.},
  langid = {english},
  file = {D:\zotero_system\storage\Z4EEDC97\ WatanabeBaturo2024_Seeded Sequential LDA A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences.pdf}
}


@article{WatanabeZhou2022,
  title = {Theory-Driven Analysis of Large Corpora: Semisupervised Topic Classification of the UN Speeches},
  shorttitle = {Theory-Driven Analysis of Large Corpora},
  author = {Watanabe, Kohei and Zhou, Yuan},
  date = {2022-04-01},
  journaltitle = {Social Science Computer Review},
  volume = {40},
  number = {2},
  pages = {346--366},
  publisher = {SAGE Publications Inc},
  issn = {0894-4393},
  doi = {10.1177/0894439320907027},
  urldate = {2023-05-03},
  abstract = {There is a growing interest in quantitative analysis of large corpora among the international relations (IR) scholars, but many of them find it difficult to perform analysis consistently with existing theoretical frameworks using unsupervised machine learning models to further develop the field. To solve this problem, we created a set of techniques that utilize a semisupervised model that allows researchers to classify documents into predefined categories efficiently. We propose a dictionary making procedure to avoid inclusion of words that are likely to confuse the model and deteriorate the its classification performance classification accuracy using a new entropy-based diagnostic tool. In our experiments, we classify sentences of the United Nations General Assembly speeches into six predefined categories using the seeded Latent Dirichlet allocation and Newsmap, which were trained with a small ?seed word dictionary? that we created following the procedure. The result shows that, while keyword dictionary can only classify 25\% of sentences, Newsmap can classify over 60\% of them accurately correctly and; its accuracy exceeds 70\% when contextual information is taken into consideration by kernel smoothing of topic likelihoods. We argue that once seed word dictionaries are created by the international relations community, semisupervised models would become more useful than unsupervised models for theory-driven text analysis.},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Watanabe_Zhou2022_Theory-Driven Analysis of Large Corpora.pdf}
}

@article{WhiteEtAl2011,
  title = {Multiple Imputation Using Chained Equations: Issues and Guidance for Practice},
  shorttitle = {Multiple Imputation Using Chained Equations},
  author = {White, Ian R. and Royston, Patrick and Wood, Angela M.},
  date = {2011-02},
  journaltitle = {Statistics in Medicine},
  volume = {30},
  number = {4},
  pages = {377--399},
  issn = {1097-0258},
  doi = {10.1002/sim.4067},
  langid = {english},
  timestamp = {2019-03-24T11:05:14Z}
}

@book{WickhamEtAl2023,
  title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
  shorttitle = {R for Data Science},
  author = {Wickham, Hadley and \c Cetinkaya-Rundel, Mine and Grolemund, Garrett},
  date = {2023-07-18},
  edition = {2nd edition},
  publisher = {O'Reilly Media},
  abstract = {Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse---a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.  You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way.  Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools.  You'll learn how to: Visualize-create plots for data exploration and communication of results Transform-discover types of variables and the tools you can use to work with them Import-get data into R and in a form convenient for analysis Program-learn R tools for solving data problems with greater clarity and ease Communicate-integrate prose, code, and results with Quarto},
  isbn = {978-1-4920-9740-2},
  langid = {english},
  pagetotal = {600}
}

@incollection{WileyWiley2019a,
  title = {Missing Data},
  booktitle = {Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization},
  author = {Wiley, Matt and Wiley, Joshua F.},
  editor = {Wiley, Matt and Wiley, Joshua F.},
  date = {2019},
  pages = {383--433},
  publisher = {Apress},
  location = {Berkeley, CA},
  doi = {10.1007/978-1-4842-2872-2_9},
  urldate = {2026-02-06},
  abstract = {Missing data is common in nearly all real-world analysis. This chapter introduces the concept of missing data formally including common ways of describing missingness. Then we discuss some of the potential ways missing data can be addressed in analysis. The main package we will use in this chapter is the mice package, one package that offers robust features for handling missing data and minimizing the impact of missing data on analysis results [95].library(checkpoint)checkpoint("2018-09-28", R.version = "3.5.1",~~project = book\_directory,~~checkpointLocation = checkpoint\_directory,~~scanForPackages = FALSE,~~scan.rnw.with.knitr = TRUE, use.knitr = TRUE)library(knitr)library(ggplot2)library(cowplot)library(lattice)library(viridis)library(VIM)library(mice)library(micemd)library(parallel)library(data.table)library(xtable)library(JWileymisc) \# has dataoptions(width = 70, digits = 2)},
  isbn = {978-1-4842-2872-2},
  langid = {english},
  keywords = {missing,multiple imputation},
  file = {D:\zotero_system\storage\TVGULJNB\ WileyEtAl 2019_Missing data.pdf}
}

@inproceedings{WilsonChew2010,
  title = {Term Weighting Schemes for Latent Dirichlet Allocation},
  booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  author = {Wilson, Andrew T. and Chew, Peter A.},
  date = {2010-06},
  pages = {465--473},
  publisher = {Association for Computational Linguistics},
  location = {Los Angeles, California},
  urldate = {2023-06-08},
  eventtitle = {NAACL-HLT 2010},
  langid = {english},
  file = {D:\Nutstore\01_Literature\Wilson_Chew2010_Term Weighting Schemes for Latent Dirichlet Allocation.pdf}
}

@article{YangFengEtAl2025,
  title = {基于大语言模型的国际信任民调数据插补},
  author = {{杨锋} and {侯煜欣} and {庞珣}},
  date = {2025},
  journaltitle = {国际政治科学},
  volume = {10},
  number = {4},
  issn = {2096-1545},
  doi = {10.16513/j.cnki.qjip.20260108.001},
  urldate = {2026-03-09},
  abstract = {近年来,预训练大语言模型的快速发展催生了区别于传统数据处理路径的``生成性建模''(generative modeling)方法,在社会科学研究中展现出广泛的创新潜力。为探索大语言模型赋能国际关系实证研究和评估其能力和可信度,本文以大语言模型进行民意调查中缺失值插补为切入点,聚焦于其中国际信任度这一高度依赖上下文的主观潜在变量,在这一具有挑战性的缺失数据处理任务上,将大模型的表现与常用且强大的现有方法进行系统性比较。本文借助``中国家庭追踪调查''(China Family Panel Studies,简称CFPS)这一纵贯性调查数据包含的丰富上下文信息,以及该调查中对国内外不同对象信任度问题所提供的比较契机,考察和对比在不同缺失机制下不同方法的能力表现,以及本土和国际大模型预测能力的优劣,以评估模型训练语料与人类反馈训练中隐含的社会语境对国内外态度预测的影响。实验结果发现,尽管生成性方法在主观民意数据插补中仍面临挑战,但在应对非随机缺失方面表现优异,其性能不逊于现有机器学习方法,且在数据保真方面能力突出。同时,本土模型在理解和模拟本地政治社会语境方面展现的能力胜过国际大模型。鉴于非随机缺失数据在国际关系研究中的常见性和挑战性,本文的发现有助于研究者应用大语言模型来应对这一重要数据问题,并展示了提升大语言模型在国际关系研究中的可信度与规范性的路径。},
  langid = {chinese},
  keywords = {missing},
  annotation = {original-container-title: Quarterly Journal of International Politics\\
foundation: 北京大学2024年度``数字与人文''专项课题的资助；\\
CLC: TP18;C915\\
dbcode: CJFQ\\
dbname: CJFDLAST2026\\
filename: GJZK202504002\\
publicationTag: 北大核心, AMI扩展, CSSCI\\
CIF: 4.111\\
AIF: 2.778},
  file = {D:\zotero_system\storage\ADJHLNU3\ 杨锋EtAl 2025_基于大语言模型的国际信任民调数据插补.pdf}
}

@article{YanYiQiao2025,
  entrysubtype = {newspaper},
  title = {当大学生遇上大模型------人工智能会成为``偷懒神器''吗},
  author = {{闫伊乔}},
  date = {2025-02-24},
  journaltitle = {人民日报},
  urldate = {2025-04-02},
  file = {D:\zotero_system\storage\2JSPUIHN\c.html}
}

@online{YanZhiHongYanFuJing2024,
  title = {最新报告出炉！2023年我国数据生产总量达32.85ZB},
  author = {{颜之宏} and {严赋憬}},
  date = {2024-05-24},
  urldate = {2024-06-17},
  langid = {chinese},
  organization = {新华网},
  file = {D:\zotero_system\storage\CYXLJ5JC\c.html}
}

@article{ZhangPan2019,
  title = {CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media},
  shorttitle = {CASM},
  author = {Zhang, Han and Pan, Jennifer},
  date = {2019-08},
  journaltitle = {Sociological Methodology},
  volume = {49},
  number = {1},
  pages = {1--57},
  issn = {0081-1750},
  doi = {10.1177/0081175019860244},
  abstract = {Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)---a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict.},
  langid = {english},
  timestamp = {2019-10-04T02:18:45Z},
  file = {D:\zotero_system\storage\N7MQGA2J\Zhang_Pan2019_CASM - A Deep-Learning Approach for Identifying Collective Action Events with.pdf}
}

@article{ZiemsEtAl2024,
  title = {Can Large Language Models Transform Computational Social Science?},
  author = {Ziems, Caleb and Held, William and Shaikh, Omar and Chen, Jiaao and Zhang, Zhehao and Yang, Diyi},
  date = {2024-03-01},
  journaltitle = {Computational Linguistics},
  volume = {50},
  number = {1},
  pages = {237--291},
  issn = {0891-2017},
  doi = {10.1162/coli_a_00502},
  urldate = {2026-01-20},
  abstract = {Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers' gold references. We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in social science analysis in partnership with humans.},
  file = {D\:\\zotero_system\\storage\\BPN4TWH3\\ ZiemsEtAl 2024_Can Large Language Models Transform Computational Social Science_ ZiemsEtAl 2024_Can Large Language Models Transform Computational Social Science.pdf;D\:\\zotero_system\\storage\\BTMV2NUX\\ ZiemsEtAl 2024_Can large language models transform computational social science.pdf;D\:\\zotero_system\\storage\\RBQFCK6G\\coli_a_00502.html}
}

@book{Zipf2016,
  title = {Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology},
  shorttitle = {Human Behavior and the Principle of Least Effort},
  author = {Zipf, George Kingsley},
  date = {2016-01-27},
  publisher = {Ravenio Books},
  abstract = {This classic is arranged as follows:~Preface~1. The Question of Practical Application.~2. The Question of Natural Science.~1. Introduction and Orientation~I. The Selection of a Path~II. The ``Singleness of the Superlative''~III. The Principle of Least Effort~IV. The Scope of the Principle: ``Tools-and-Jobs''~V. Previous Studies~VI. Prospectus~2. On the Economy of Words~I. In Medias Res: Vocabulary Usage, and the Forces of Unification and Diversification~II. The Question of Vocabulary Balance~III. The Orderly Distribution of Meanings~IV. The Integrality of Frequencies~V. The Integrality of Rank~VI. The Length of Intervals Between Repetitions~VII. The Problem of Spreading Work Over Time (The Even Distribution of Work Over Time)~3. Formal Semantic Balance and the Economy of Evolutionary Process~I. The ``Minimum Equation'' Of Arrangement~II. The Law of Abbreviation of Words~III. The Law of Diminishing Returns of Tools~IV. The Law of Diminishing Returns of Words~4. Children's Verbalizations and the ``Origin of Speech''~I. The Problem~II. Quantitative Data~III. Theoretical Discussion of the ``Origin'' Of Speech~IV. Summary~5. Language as Sensation and Mentation~I. The Comparative Conservatism of Tools in the Risks and Opportunities of the Environment~II. The Economy of Sensation~III. Mentation: The Correlation of Sensory Data~IV. A Mind as a Unit Semantic System~V. Intellectual Rigidity and Death: Miscellanea~V. Summary: The N Minimum~6. The Ego as the ``Origin'' Of a Frame of Reference~I. A Definition of an Organism~II. The Biosocial Population of Organisms~III. The Economy of Procreation~IV. The Synchrony of the Biosocial Continuum~7. Mind and the Economy of Symbolic Process: Sex, Culture, and Schizophrenia~I. Human Sexual Activity~II. The Economy of Symbolic Process (Substitution~III. Culture, Society, and the Superego~IV. Autism and the Confusion of Kinds of Reality~V. On Schizophrenic Speech~VI. Semantic Dynamics: Summary~VI. Language and the Structure of the Personality~8. The Language of Dreams and of Art~I. The Language of Dreams~II. The Language of Art~III. Language and the Structure of the Personality: Mary of Part One~9. The Economy of Geography~I. A Lemma in Which a Number of Human Beings Becomes Increasingly More Organized~II. The Hypothesis of the ``Minimum Equation''~III. Empiric Tests~IV. Concluding Remarks~10. Intranational and International Cooperation and Conflict~I. Canadian Data~II. Unstable and Stable Intranational Conditions~III. Stable and Unstable International Equilibria~11. The Distribution of Economic Power and Social Status~I. Theoretical Considerations~II. Empiric Data~III. The Interaction Between Individuals: Dominance and Submission~IV. Summary~12. Prestige Symbols and Cultural Vogues~I. Theoretical Considerations~II. Pioneer Empiric Data~III. Musical Composers and Compositions~IV. Samples of Congressional Action~V. Summary},
  langid = {english},
  pagetotal = {637}
}

@article{ornstein2025train,
  title={How to train your stochastic parrot: Large language models for political texts},
  author={Ornstein, Joseph T and Blasingame, Elise N and Truscott, Jake S},
  journal={Political Science Research and Methods},
  volume={13},
  number={2},
  pages={264--281},
  year={2025},
  publisher={Cambridge University Press}
}

@article{gilardi2023chatgpt,
  title={ChatGPT outperforms crowd workers for text-annotation tasks},
  author={Gilardi, Fabrizio and Alizadeh, Meysam and Kubli, Ma{\"e}l},
  journal={Proceedings of the National Academy of Sciences},
  volume={120},
  number={30},
  pages={e2305016120},
  year={2023},
  publisher={National Academy of Sciences}
}

@inproceedings{stammbach2023revisiting,
  title={Revisiting automated topic model evaluation with large language models},
  author={Stammbach, Dominik and Zouhar, Vil{\'e}m and Hoyle, Alexander and Sachan, Mrinmaya and Ash, Elliott},
  booktitle={Proceedings of the 2023 conference on empirical methods in natural language processing},
  pages={9348--9357},
  year={2023}
}

@article{barrie2024prompt,
  title={Prompt stability scoring for text annotation with large language models},
  author={Barrie, Christopher and Palaiologou, Elli and T{\~A}{\c{k}}rnberg, Petter},
  journal={arXiv preprint arXiv:2407.02039},
  year={2024}
}

@article{Stone1962,
    author    = {Stone, Philip J. and Bales,
  Robert F. and Namenwirth, J. Zvi and
  Ogilvie, Daniel M.},
    title     = {The General Inquirer: A
  Computer System for Content Analysis and
  Retrieval Based on the Sentence as a Unit
  of Information},
    journal   = {Behavioral Science},
    volume    = {7},
    number    = {4},
    pages     = {484--498},
    year      = {1962},
    doi       = {10.1002/bs.3830070412}
  }

@article{Dillion2023,
    author    = {Dillion, Danica and Tandon,
  Niket and Gu, Yuling and Gray, Kurt},
    title     = {Can {AI} Language Models
  Replace Human Participants?},
    journal   = {Trends in Cognitive
  Sciences},
    volume    = {27},
    number    = {7},
    pages     = {597--600},
    year      = {2023},
    doi       = {10.1016/j.tics.2023.04.008}
  }
