references.bib

@inproceedings{van_de_camp_link_2011,
	location = {Portland, Oregon},
	title = {A Link to the Past: Constructing Historical Social Networks},
	url = {https://aclanthology.org/W11-1708},
	series = {Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis ({WASSA} 2.011)},
	pages = {61--69},
	publisher = {Association for Computational Linguistics},
	author = {van de Camp, Matje and van den Bosch, Antal},
	date = {2011-06},
	file = {Full Text:/Users/shiyishen/Zotero/storage/W6XW2YHN/van de Camp and van den Bosch - 2011 - A Link to the Past Constructing Historical Social.pdf:application/pdf},
}

@article{thomas_get_8,
	title = {Get out the vote: Determining support or opposition from Congressional floor-debate transcripts},
	url = {https://aclanthology.org/W06-1639.pdf},
	author = {Thomas, Matt and Pang, Bo and Lee, Lillian},
	date = {0008-12},
	file = {Full Text:/Users/shiyishen/Zotero/storage/3JZFCPYQ/Thomas et al. - 2008 - Get out the vote Determining support or oppositio.pdf:application/pdf},
}

@book{somasundaran_recognizing_2009,
	title = {Recognizing Stances in Online Debates},
	url = {https://dl.acm.org/doi/pdf/10.5555/1687878.1687912},
	author = {Somasundaran, Swapna and Wiebe, Janyce},
	date = {2009},
	doi = {10.3115/1687878.1687912},
	file = {Full Text:/Users/shiyishen/Zotero/storage/67BNF5CQ/Somasundaran and Wiebe - 2009 - Recognizing Stances in Online Debates.pdf:application/pdf},
}

@inproceedings{sim_measuring_2013,
	location = {Seattle, Washington, {USA}},
	title = {Measuring Ideological Proportions in Political Speeches},
	url = {https://aclanthology.org/D13-1010},
	series = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
	pages = {91--101},
	publisher = {Association for Computational Linguistics},
	author = {Sim, Yanchuan and Acree, Brice D. L. and Gross, Justin H. and Smith, Noah A.},
	date = {2013-10},
	file = {Full Text:/Users/shiyishen/Zotero/storage/DBBIAEXR/Sim et al. - 2013 - Measuring Ideological Proportions in Political Spe.pdf:application/pdf},
}

@article{poole_d-nominate_1,
	title = {D-Nominate after 10 Years},
	volume = {26},
	doi = {10.2307/440401},
	journaltitle = {Legislative Studies Quarterly},
	author = {Poole, Keith and Rosenthal, Howard},
	date = {0001-01},
}

@article{poole_spatial_8,
	title = {A Spatial Model for Legislative Roll Call Vote Analysis},
	volume = {29},
	doi = {10.2307/2111172},
	journaltitle = {American Journal of Political Science},
	author = {Poole, Keith and Rosenthal, Howard},
	date = {0008-01},
	file = {Full Text:/Users/shiyishen/Zotero/storage/4Y627UKX/Poole and Rosenthal - 2008 - A Spatial Model for Legislative Roll Call Vote Ana.pdf:application/pdf},
}

@article{poole_political-economic_1,
	title = {A Political-Economic History of Roll-Call Voting},
	author = {Poole, Keith},
	date = {0001-01},
}

@article{ozsoy_text_8,
	title = {Text summarization using Latent Semantic Analysis},
	volume = {37},
	url = {https://journals.sagepub.com/doi/10.1177/0165551511408848},
	doi = {10.1177/0165551511408848},
	pages = {405--417},
	journaltitle = {J. Information Science},
	author = {Ozsoy, Makbule and Alpaslan, Ferda and Cicekli, Ilyas},
	date = {0008-11},
	file = {Submitted Version:/Users/shiyishen/Zotero/storage/6DN2ZAPP/Ozsoy et al. - 2008 - Text summarization using Latent Semantic Analysis.pdf:application/pdf},
}

@book{mullen_preliminary_2006,
	title = {A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse},
	url = {https://malouf.sdsu.edu/pubs/aaai-politics.pdf},
	pagetotal = {159-162},
	author = {Mullen, Tony and Malouf, Robert},
	date = {2006},
	file = {Full Text:/Users/shiyishen/Zotero/storage/P7FI3H8H/Mullen and Malouf - 2006 - A Preliminary Investigation into Sentiment Analysi.pdf:application/pdf},
}

@article{hart_campaign_7,
	title = {Campaign Talk: Why Elections Are Good for Us},
	pages = {1--307},
	journaltitle = {Campaign Talk: Why Elections Are Good for Us},
	author = {Hart, Roderick},
	date = {0007-01},
}

@book{gerrish_predicting_2011,
	title = {Predicting Legislative Roll Calls from Text},
	url = {https://icml.cc/2011/papers/333_icmlpaper.pdf},
	pagetotal = {489-496},
	author = {Gerrish, Sean and Blei, David},
	date = {2011},
	note = {Journal Abbreviation: Proceedings of the 28th International Conference on Machine Learning, {ICML} 2011
Publication Title: Proceedings of the 28th International Conference on Machine Learning, {ICML} 2011},
	file = {Full Text:/Users/shiyishen/Zotero/storage/N2KPDQVE/Gerrish and Blei - 2011 - Predicting Legislative Roll Calls from Text.pdf:application/pdf},
}

@article{gerrish_how_1,
	title = {How they vote: Issue-adjusted models of legislative behavior},
	volume = {4},
	url = {https://proceedings.neurips.cc/paper/2012/file/193002e668758ea9762904da1a22337c-Paper.pdf},
	pages = {2753--2761},
	journaltitle = {Advances in Neural Information Processing Systems},
	author = {Gerrish, S. M. and Blei, D. M.},
	date = {0001-01},
	file = {Full Text:/Users/shiyishen/Zotero/storage/JSSB6H5X/Gerrish and Blei - 2001 - How they vote Issue-adjusted models of legislativ.pdf:application/pdf},
}

@article{gentzkow_media_4,
	title = {Media Bias and Reputation},
	volume = {114},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=642362},
	doi = {10.2139/ssrn.642362},
	pages = {280--316},
	journaltitle = {Journal of Political Economy},
	author = {Gentzkow, Matthew and Shapiro, Jesse},
	date = {0004-01},
	file = {Full Text:/Users/shiyishen/Zotero/storage/HTVHS66K/Gentzkow and Shapiro - 2004 - Media Bias and Reputation.pdf:application/pdf},
}

@book{fader_mavenrank_2007,
	title = {{MavenRank}: Identifying Influential Members of the {US} Senate Using Lexical Centrality},
	pagetotal = {658-666},
	author = {Fader, Anthony and Radev, Dragomir and Crespin, Michael and Monroe, Burt and Quinn, Kevin and Colaresi, Michael},
	date = {2007},
}

@article{downs_economic_1,
	title = {A Economic Theory of Democracy},
	volume = {13},
	doi = {10.2307/2109186},
	journaltitle = {The Western Political Quarterly},
	author = {Downs, Anthony},
	date = {0001-01},
	file = {Full Text:/Users/shiyishen/Zotero/storage/9EIPC94I/Downs - 2001 - A Economic Theory of Democracy.pdf:application/pdf},
}

@article{diermeier_language_1,
	title = {Language and Ideology in Congress},
	volume = {42},
	url = {https://www.cambridge.org/core/services/aop-cambridge-core/content/view/1063F5509BC2ABC3F9A0E164E58157EE/S0007123411000160a.pdf/div-class-title-language-and-ideology-in-congress-div.pdf},
	doi = {10.1017/S0007123411000160},
	pages = {31--55},
	journaltitle = {British Journal of Political Science},
	author = {{Diermeier} and Godbout, Jean-Francois and Yu, Bei and Kaufmann, Stefan},
	date = {0001-01},
	file = {Full Text:/Users/shiyishen/Zotero/storage/42WLNLTY/Diermeier et al. - 2001 - Language and Ideology in Congress.pdf:application/pdf},
}

@article{clinton_statistical_4,
	title = {The Statistical Analysis of Roll Call Data},
	volume = {98},
	url = {https://www.cambridge.org/core/services/aop-cambridge-core/content/view/75DBC6645F85A764AE9E5DBF468AB813/S0003055404001194a.pdf/div-class-title-the-statistical-analysis-of-roll-call-data-div.pdf},
	doi = {10.1017/S0003055404001194},
	journaltitle = {American Political Science Review - {AMER} {POLIT} {SCI} {REV}},
	author = {Clinton, Joshua and Jackman, Simon and Rivers, Douglas},
	date = {0004-04},
	file = {Full Text:/Users/shiyishen/Zotero/storage/4SDEY649/Clinton et al. - 2004 - The Statistical Analysis of Roll Call Data.pdf:application/pdf},
}

@article{carbonell_politicsautomated_3,
	title = {{POLITICS}:automated ideological reasoning},
	volume = {2},
	doi = {10.1016/S0364-0213(78)80060-3},
	pages = {27--51},
	journaltitle = {Cognitive Science},
	author = {Carbonell, Jaime},
	date = {0003-01},
}

@book{charteris-black_politicians_2011,
	title = {Politicians and rhetoric: The persuasive power of metaphor, second edition},
	isbn = {978-0-230-25165-6},
	pagetotal = {1-370},
	author = {Charteris-Black, Jonathan},
	date = {2011},
	doi = {10.1057/9780230319899},
	file = {Full Text:/Users/shiyishen/Zotero/storage/KNWUWLT5/Charteris-Black - 2011 - Politicians and rhetoric The persuasive power of .pdf:application/pdf},
}

@article{black_rationale_1,
	title = {On The Rationale of Group Decision Making},
	volume = {56},
	doi = {10.1086/256633},
	journaltitle = {Journal of Political Economy - J {POLIT} {ECON}},
	author = {Black, Duncan},
	date = {0001-01},
}

@inproceedings{anand_cats_2011,
	location = {Portland, Oregon},
	title = {Cats Rule and Dogs Drool!: Classifying Stance in Online Debate},
	url = {https://aclanthology.org/W11-1701},
	series = {Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis ({WASSA} 2.011)},
	pages = {1--9},
	publisher = {Association for Computational Linguistics},
	author = {Anand, Pranav and Walker, Marilyn and Abbott, Rob and Fox Tree, Jean E. and Bowmani, Robeson and Minor, Michael},
	date = {2011-06},
	file = {Full Text:/Users/shiyishen/Zotero/storage/XREVQE9P/Anand et al. - 2011 - Cats Rule and Dogs Drool! Classifying Stance in O.pdf:application/pdf},
}

@article{abelson_computer_1965,
	title = {Computer Simulation of Individual Belief Systems*: The Memory Structure Cognitive Processes System Control of the Response Processes Effects of the Processes on the System Putting a Memory Structure into the System A Preliminary Empirical Study References},
	volume = {8},
	issn = {00027642},
	url = {https://www.proquest.com/scholarly-journals/computer-simulation-individual-belief-systems/docview/194652571/se-2?accountid=9703},
	abstract = {In this paper we sketch an attempt at computer simulation of individual belief systems. Although rooted in a series of earlier formulations (Abelson and Rosenberg [1958],1 Abelson [1959],2 Rosenberg and Abelson [1960],3 Abelson [193]), this complex project has not heretofore been totally outlined in print. We shall begin with several clarifying comments on the nature of our goals and the problems we have faced, thence proceeding Further and...},
	pages = {7},
	number = {9},
	journaltitle = {The American Behavioral Scientist (pre-1986)},
	author = {Abelson, Robert P. and Carroll, J. Douglas},
	date = {1965-05-09},
	keywords = {Computer simulation, Memory, Psychology, Semantics},
}

@inproceedings{culotta_integrating_2006,
	location = {New York City, {USA}},
	title = {Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text},
	url = {https://aclanthology.org/N06-1038},
	eventtitle = {{NAACL}-{HLT} 2006},
	pages = {296--303},
	booktitle = {Proceedings of the Human Language Technology Conference of the {NAACL}, Main Conference},
	publisher = {Association for Computational Linguistics},
	author = {Culotta, Aron and {McCallum}, Andrew and Betz, Jonathan},
	urldate = {2023-09-26},
	date = {2006-06},
	file = {Full Text PDF:/Users/shiyishen/Zotero/storage/H2JLRCB9/Culotta et al. - 2006 - Integrating Probabilistic Extraction Models and Da.pdf:application/pdf},
}

@article{jackman_multidimensional_2001,
	title = {Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking},
	volume = {9},
	issn = {1047-1987, 1476-4989},
	url = {https://www.cambridge.org/core/journals/political-analysis/article/multidimensional-analysis-of-roll-call-data-via-bayesian-simulation-identification-estimation-inference-and-model-checking/C7459FB85A5618B471FCE1E130FA8A48},
	doi = {10.1093/polana/9.3.227},
	shorttitle = {Multidimensional Analysis of Roll Call Data via Bayesian Simulation},
	abstract = {Vote-specific parameters are often by-products of roll call analysis, the primary goal being the measurement of legislators' ideal points. But these vote-specific parameters are more important in higher-dimensional settings: prior restrictions on vote parameters help identify the model, and researchers often have prior beliefs about the nature of the dimensions underlying the proposal space. Bayesian methods provide a straightforward and rigorous way for incorporating these prior beliefs into roll call analysis. I demonstrate this by exploiting the close connections among roll call analysis, item-response models, and “full-information” factor analysis. Vote-specific discrimination parameters are equivalent to factor loadings, and as in factor analysis, they (1) enable researchers to discern the substantive content of the recovered dimensions, (2) can be used for assessing dimensionality and model checking, and (3) are an obvious vehicle for introducing and testing researchers' prior beliefs about the dimensions. Bayesian simulation facilitates these uses of discrimination parameters, by simplifying estimation and inference for the massive number of parameters generated by roll call analysis.},
	pages = {227--241},
	number = {3},
	journaltitle = {Political Analysis},
	author = {Jackman, Simon},
	urldate = {2023-09-27},
	date = {2001-01},
	langid = {english},
	note = {Publisher: Cambridge University Press},
	file = {Full Text PDF:/Users/shiyishen/Zotero/storage/NCZ65PPT/Jackman - 2001 - Multidimensional Analysis of Roll Call Data via Ba.pdf:application/pdf},
}

@article{jackman_estimation_2000,
	title = {Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation},
	volume = {8},
	issn = {1047-1987, 1476-4989},
	url = {https://www.cambridge.org/core/journals/political-analysis/article/abs/estimation-and-inference-are-missing-data-problems-unifying-social-science-statistics-via-bayesian-simulation/7BAC50088882C072D08DD06173C23303},
	doi = {10.1093/oxfordjournals.pan.a029818},
	shorttitle = {Estimation and Inference Are Missing Data Problems},
	abstract = {Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item—response models for the measurement of respondent's levels of political information in public opinion surveys, the estimation and analysis of legislators' ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections},
	pages = {307--332},
	number = {4},
	journaltitle = {Political Analysis},
	author = {Jackman, Simon},
	urldate = {2023-09-27},
	date = {2000-07},
	langid = {english},
	note = {Publisher: Cambridge University Press},
}

@misc{vafa_text-based_2020,
	title = {Text-Based Ideal Points},
	url = {http://arxiv.org/abs/2005.04232},
	abstract = {Ideal point models analyze lawmakers' votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model ({TBIP}), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the {TBIP} with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the {TBIP} separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the {TBIP} can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-to-moderate spectrum.},
	number = {{arXiv}:2005.04232},
	publisher = {{arXiv}},
	author = {Vafa, Keyon and Naidu, Suresh and Blei, David M.},
	urldate = {2023-10-05},
	date = {2020-07-21},
	eprinttype = {arxiv},
	eprint = {2005.04232 [cs, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/Users/shiyishen/Zotero/storage/4QXFLYB4/2005.html:text/html;Full Text PDF:/Users/shiyishen/Zotero/storage/TPLR648D/Vafa 等 - 2020 - Text-Based Ideal Points.pdf:application/pdf},
}

@article{gerrish_predicting_nodate,
	title = {Predicting Legislative Roll Calls from Text},
	abstract = {We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy.},
	author = {Gerrish, Sean M and Blei, David M},
	langid = {english},
	file = {Gerrish 和 Blei - Predicting Legislative Roll Calls from Text.pdf:/Users/shiyishen/Zotero/storage/MY45BNW3/Gerrish 和 Blei - Predicting Legislative Roll Calls from Text.pdf:application/pdf},
}

@article{briatte_recovering_nodate,
	title = {Recovering the French Party Space from Twitter Data},
	abstract = {This study explores the possibility to retrieve information on partisan polarization from data generated by online social media users. The specific application that we pursue consists in placing a sample of over 1,000 French politicians on a unidimensional left-right scale by using their followers on Twitter as a proxy for their relative ideological positions. The methodology that we use to that end closely replicates that of Barberá (2015), who developed a Bayesian Spatial Following model to retrieve such ideal point estimates in the United States and in five European countries. Our results concur with existing measures of the French party space, and yield additional insights into the behaviour of ideologically extreme social media users.},
	author = {Briatte, François and Gallic, Ewen},
	langid = {english},
	file = {Briatte 和 Gallic - Recovering the French Party Space from Twitter Dat.pdf:/Users/shiyishen/Zotero/storage/I39N9MND/Briatte 和 Gallic - Recovering the French Party Space from Twitter Dat.pdf:application/pdf},
}

@misc{barbera_replication_2015,
	title = {Replication Data for: Tweeting from Left to Right: Is Online Political Communication More Than an Echo Chamber?},
	url = {https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/F9ICHH},
	doi = {10.7910/DVN/F9ICHH},
	shorttitle = {Replication Data for},
	abstract = {We estimated ideological preferences of 3.8 million Twitter users and, using a dataset of 150 million tweets concerning 12 political and non-politi...},
	publisher = {Harvard Dataverse},
	author = {Barbera, Pablo and Jost, John and Nagler, Jonathan and Tucker, Joshua and Bonneau, Richard},
	urldate = {2023-10-05},
	date = {2015-06-09},
	langid = {english},
}

@article{barbera_tweeting_2015,
	title = {Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?},
	volume = {26},
	issn = {0956-7976},
	url = {https://doi.org/10.1177/0956797615594620},
	doi = {10.1177/0956797615594620},
	shorttitle = {Tweeting From Left to Right},
	abstract = {We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues, explored whether online communication resembles an “echo chamber” (as a result of selective exposure and ideological segregation) or a “national conversation.” We observed that information was exchanged primarily among individuals with similar ideological preferences in the case of political issues (e.g., 2012 presidential election, 2013 government shutdown) but not many other current events (e.g., 2013 Boston Marathon bombing, 2014 Super Bowl). Discussion of the Newtown shootings in 2012 reflected a dynamic process, beginning as a national conversation before transforming into a polarized exchange. With respect to both political and nonpolitical issues, liberals were more likely than conservatives to engage in cross-ideological dissemination; this is an important asymmetry with respect to the structure of communication that is consistent with psychological theory and research bearing on ideological differences in epistemic, existential, and relational motivation. Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage.},
	pages = {1531--1542},
	number = {10},
	journaltitle = {Psychological Science},
	shortjournal = {Psychol Sci},
	author = {Barberá, Pablo and Jost, John T. and Nagler, Jonathan and Tucker, Joshua A. and Bonneau, Richard},
	urldate = {2023-10-05},
	date = {2015-10-01},
	langid = {english},
	note = {Publisher: {SAGE} Publications Inc},
}

@article{jackman_multidimensional_2001-1,
	title = {Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking},
	volume = {9},
	issn = {1047-1987, 1476-4989},
	url = {https://www.cambridge.org/core/product/identifier/S1047198700003818/type/journal_article},
	doi = {10.1093/polana/9.3.227},
	shorttitle = {Multidimensional Analysis of Roll Call Data via Bayesian Simulation},
	abstract = {Vote-specific parameters are often by-products of roll call analysis, the primary goal being the measurement of legislators' ideal points. But these vote-specific parameters are more important in higher-dimensional settings: prior restrictions on vote parameters help identify the model, and researchers often have prior beliefs about the nature of the dimensions underlying the proposal space. Bayesian methods provide a straightforward and rigorous way for incorporating these prior beliefs into roll call analysis. I demonstrate this by exploiting the close connections among roll call analysis, item-response models, and “full-information” factor analysis. Vote-specific discrimination parameters are equivalent to factor loadings, and as in factor analysis, they (1) enable researchers to discern the substantive content of the recovered dimensions, (2) can be used for assessing dimensionality and model checking, and (3) are an obvious vehicle for introducing and testing researchers' prior beliefs about the dimensions. Bayesian simulation facilitates these uses of discrimination parameters, by simplifying estimation and inference for the massive number of parameters generated by roll call analysis.},
	pages = {227--241},
	number = {3},
	journaltitle = {Political Analysis},
	shortjournal = {Polit. anal.},
	author = {Jackman, Simon},
	urldate = {2023-10-05},
	date = {2001-01},
	langid = {english},
	file = {Jackman - 2001 - Multidimensional Analysis of Roll Call Data via Ba.pdf:/Users/shiyishen/Zotero/storage/MSRJW9MN/Jackman - 2001 - Multidimensional Analysis of Roll Call Data via Ba.pdf:application/pdf},
}