Bibliography – Sorted by Author

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Ahonen, Pertti. “Institutionalizing Big Data Methods in Social and Political Research                                                    ,                                                            Institutionalizing Big Data Methods in Social and Political Research.” Big Data & Society 2, no. 2 (2015): 2053951715591224. Cite
Aletras, Nikolaos, and Mark Stevenson. “Labelling Topics Using Unsupervised Graph-Based Methods.” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 631–36. Baltimore, Maryland: Association for Computational Linguistics, 2014. Cite Download
AllSides. “Media Bias Ratings.” AllSides, March 6, 2018. Cite
AlSumait, Loulwah, Daniel Barbará, James Gentle, and Carlotta Domeniconi. “Topic Significance Ranking of LDA Generative Models.” In Proceedings of the 2009th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I, 67–82. ECMLPKDD’09. Berlin, Heidelberg: Springer-Verlag, 2009. Cite Download
American Academy of Arts and Sciences, Commission on the Humanities and Social Sciences. “The Heart of the Matter.” Cambridge, MA, 2015. Cite
Andrews, Mark, and Gabriella Vigliocco. “The Hidden Markov Topic Model: A Probabilistic Model of Semantic Representation.” Topics in Cognitive Science 2, no. 1 (2010): 101–113. Cite
Arora, Sanjeev, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. “A Practical Algorithm for Topic Modeling with Provable Guarantees.” ArXiv:1212.4777 [Cs, Stat], December 19, 2012. Cite
Arun, R., V. Suresh, C. E. Veni Madhavan, and M. N. Narasimha Murthy. “On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations.” In Advances in Knowledge Discovery and Data Mining, edited by Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, and Vikram Pudi, 391–402. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. Cite
Bail, Christopher. “The Cultural Environment: Measuring Culture With Big Data.” ResearchGate, 2014. Cite
Bail, Christopher A. “Topic Modeling.” Course site. Text as Data, 2018. Cite
Belfiore, Eleonora. “‘Impact’, ‘Value’ and ‘Bad Economics’: Making Sense of the Problem of Value in the Arts and Humanities.” Arts and Humanities in Higher Education 14, no. 1 (2015): 95–110. Cite Download
Belfiore, Eleonora, ed. Humanities in the Twenty-First Century: Beyond Utility and Markets. Basingstoke: Palgrave Macmillan, 2013. Cite
Bhatia, Shraey, Jey Han Lau, and Timothy Baldwin. “Automatic Labelling of Topics with Neural Embeddings.” ArXiv:1612.05340 [Cs], 2016. Cite
Binder, J. M., and C. Jennings. “Visibility and Meaning in Topic Models and 18th-Century Subject Indexes.” Literary and Linguistic Computing 29, no. 3 (2014): 405–11. Cite
Bischof, Jonathan M., and Edoardo M. Airoldi. “Summarizing Topical Content with Word Frequency and Exclusivity.” In Proceedings of the 29th International Coference on International Conference on Machine Learning, 9–16. ICML’12. USA: Omnipress, 2012. Cite
Blei, David M. “Topic Modeling and Digital Humanities.” Journal of Digital Humanities, 2012. Cite
Blei, David M. “Probabilistic Topic Models.” Communications of the ACM 55, no. 4 (2012): 77. Cite Download
Blei, David M., and John D. Lafferty. “Dynamic Topic Models.” In Proceedings of the 23rd International Conference on Machine Learning  - ICML ’06, 113–20. Pittsburgh, Pennsylvania: ACM Press, 2006. Cite
Bod, Rens. A New History of the Humanities: The Search for Principles and Patterns from Antiquity to the Present. Translated by Lynn Richards. Oxford: Oxford University Press, 2013. Cite
Bod, Rens, Julia Kursell, Jaap Maat, and Thijs Weststeijn. History of Humanities Journal. University Of Chicago Press, 2016. Cite
Boyd-Graber, Jordan, and David M. Blei. “Multilingual Topic Models for Unaligned Text.” In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 75–82. AUAI Press, 2009. Cite
Boyd-Graber, Jordan L., and David M. Blei. “Syntactic Topic Models.” In Advances in Neural Information Processing Systems, 185–192, 2009. Cite
Boyd-Graber, Jordan, and Philip Resnik. “Holistic Sentiment Analysis across Languages: Multilingual Supervised Latent Dirichlet Allocation.” In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 45–55. Association for Computational Linguistics, 2010. Cite
Boyd-Graber, Jordan, David Blei, and Xiaojin Zhu. “A Topic Model for Word Sense Disambiguation.” In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 1024–33, 2007. Cite
Boyer, Ryan. “A Human-in-the-Loop Methodology For Applying Topic Models to Identify Systems Thinking and to Perform Systems Analysis.” University of Virginia, 2016. Cite Download
Buurma, Rachel Sagner. “The Fictionality of Topic Modeling: Machine Reading Anthony Trollope’s Barsetshire Series.” Big Data & Society 2, no. 2 (2015): 2053951715610591. Cite
Cao, L., and Li Fei-Fei. “Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes.” In 2007 IEEE 11th International Conference on Computer Vision, 1–8, 2007. Cite
Cao, Juan, Tian Xia, Jintao Li, Yongdong Zhang, and Sheng Tang. “A Density-Based Method for Adaptive LDA Model Selection.” Neurocomputing, Advances in Machine Learning and Computational Intelligence, 72, no. 7 (2009): 1775–81. Cite
Champlin, Dell, and Janet Knoedler. “Operating in the Public Interest or in Pursuit of Private Profits? News in the Age of Media Consolidation.” Journal of Economic Issues 36, no. 2 (2002): 459–68. Cite
Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan L. Boyd-graber, and David M. Blei. “Reading Tea Leaves: How Humans Interpret Topic Models.” In Advances in Neural Information Processing Systems 22, edited by Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, 288–296. Curran Associates, Inc., 2009. Cite Download
Chen, Edwin. “Introduction to Latent Dirichlet Allocation.” Edwin Chen, 2011. Cite
Chuang, Jason, Sonal Gupta, Christopher D. Manning, and Jeffrey Heer. “Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment.” In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, III–612–III–620. ICML’13. Atlanta, GA, USA:, 2013. Cite
Chuang, Jason, Christopher D. Manning, and Jeffrey Heer. “Termite: Visualization Techniques for Assessing Textual Topic Models.” In Proceedings of the International Working Conference on Advanced Visual Interfaces, 74–77. AVI ’12. New York, NY, USA: ACM, 2012. Cite
Chuang, Jason, Margaret E. Roberts, Brandon M. Stewart, Rebecca Weiss, Dustin Tingley, Justin Grimmer, and Jeffrey Heer. “TopicCheck: Interactive Alignment for Assessing Topic Model Stability.” In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 175–84. Denver: Association for Computational Linguistics, 2015. Cite Download
Chuang, Jason, Daniel Ramage, Christopher Manning, and Jeffrey Heer. “Interpretation and Trust: Designing Model-Driven Visualizations for Text Analysis.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 443–452. CHI ’12. New York, NY, USA: ACM, 2012. Cite
Collini, Stefan. “Seeing a Specialist: The Humanities as Academic Disciplines.” Past & Present 229, no. 1 (2015): 271–81. Cite
Collini, Stefan. “Seeing a Specialist: The Humanities as Academic Disciplines.” Past & Present 229, no. 1 (2015): 271–81. Cite
Conard, Edward. “We Don’t Need More Humanities Majors.” AEI, 2013. Cite
Curran, Ben, Demival Vasques Filho, Kyle Higham, and Elisenda Ortiz. “Look Who’s Talking: Two-Mode Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches.” PLoS ONE 13, no. 6 (2018): 1–16. Cite
Dallal, Ahmad. “The Crisis of the Academic Humanities in the Arab World.” Comparative Studies of South Asia, Africa and the Middle East 37, no. 1 (2017): 134–41. Cite
Davidson, Cathy N. “Humanities 2.0: Promise, Perils, Predictions.” PMLA 123, no. 3 (2008): 707–17. Cite
Davidson, Cathy N., and David Theo Goldberg. “A Manifesto for the Humanities in a Technological Age.” The Chronicle of Higher Education, 2004. Cite
Deveaud, Romain, Eric SanJuan, and Patrice Bellot. “Accurate and Effective Latent Concept Modeling for Ad Hoc Information Retrieval.” Document Numérique 17, no. 1 (2014): 61–84. Cite Download
Dewitt, Anne. “Advances in the Visualization of Data: The Network of Genre in the Victorian Periodical Press.” Victorian Periodicals Review 48, no. 2 (July 6, 2015): 161–82. Cite
Digital Humanities Lab at Georgia Tech. “TOME: Interactive TOpic Model and MEtadata Visualization.” DH LAB, 2014. Cite
DiMaggio, Paul, Manish Nag, and David Blei. “Exploiting Affinities between Topic Modeling and the Sociological Perspective on Culture: Application to Newspaper Coverage of U.S. Government Arts Funding.” Poetics 41, no. 6 (December 1, 2013): 570–606. Cite
Dobson, James E. “Can An Algorithm Be Disturbed?: Machine Learning, Intrinsic Criticism, and the Digital Humanities.” College Literature 42, no. 4 (October 6, 2015): 543–64. Cite
Doshi-Velez, Finale, and Been Kim. “Towards A Rigorous Science of Interpretable Machine Learning.” ArXiv:1702.08608 [Cs, Stat], 2017. Cite
Drout, Michael, and Leah Smith. “How to Read a Dendogram.” Lexomics Project, 2012. Cite
Eidelman, Vladimir, Jordan Boyd-Graber, and Philip Resnik. “Topic Models for Dynamic Translation Model Adaptation.” In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, 115–119. Association for Computational Linguistics, 2012. Cite