Bibliography – Topic Model (Applied Examples)

Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all) Distant Reading | Cultural Analytics | | Sociocultural Approaches | Topic Modeling in DH | Non-consumptive Use


Thomas, Lindsay, and Abigail Droge. “The Humanities in Public: A Computational Analysis of US National and Campus Newspapers.” Journal of Cultural Analytics 7, no. 1 (2022): 36–80. https://culturalanalytics.org/article/32036-the-humanities-in-public-a-computational-analysis-of-us-national-and-campus-newspapers. Cite
Lee, James Jaehoon, and Joshua Beckelhimer. “Anthropocene and Empire: Discourse Networks of the Human Record.” PMLA/Publications of the Modern Language Association of America 135, no. 1 (2020): 110–29. https://doi.org/10.1632/pmla.2020.135.1.110. Cite
Enderle, Scott. Topic Modeling Tool, 2019. https://github.com/senderle/topic-modeling-tool. Cite
Walter, Dror, and Yotam Ophir. “News Frame Analysis: An Inductive Mixed-Method Computational Approach.” Communication Methods and Measures 13, no. 4 (2019): 248–66. https://doi.org/10.1080/19312458.2019.1639145. Cite
Ylä-Anttila, Tuukka, Veikko Eranti, and Anna Kukkonen. “Topic Modeling as a Method for Frame Analysis: Data Mining the Climate Change Debate in India and the USA.” Preprint. SocArXiv, 2018. https://doi.org/10.31235/osf.io/dgc38. 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. https://doi.org/10.1371/journal.pone.0199072. Cite
Erlin, Matt. “Topic Modeling, Epistemology, and the English and German Novel.” Journal of Cultural Analytics, 2017. https://doi.org/10.22148/16.014. Cite
Pashakhin, Sergey. “Topic Modeling for Frame Analysis of News Media.” In Proceeding of the Ainl Fruct 2016 Conference, 103–5. Saint-Petersburg, 2016. https://www.researchgate.net/publication/321771922_Topic_Modeling_for_Frame_Analysis_of_News_Media. Cite
Hua, Ting, Yue Ning, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. “Topical Analysis of Interactions Between News and Social Media.” In Thirtieth AAAI Conference on Artificial Intelligence, 2016. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12301. Cite
Törnberg, Anton, and Petter Törnberg. “Combining CDA and Topic Modeling: Analyzing Discursive Connections between Islamophobia and Anti-Feminism on an Online Forum , Combining CDA and Topic Modeling: Analyzing Discursive Connections between Islamophobia and Anti-Feminism on an Online Forum.” Discourse & Society 27, no. 4 (2016): 401–22. https://doi.org/10.1177/0957926516634546. Cite
Guo, Lei, Chris J. Vargo, Zixuan Pan, Weicong Ding, and Prakash Ishwar. “Big Social Data Analytics in Journalism and Mass Communication , Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling , Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling.” Journalism & Mass Communication Quarterly 93, no. 2 (2016): 332–59. https://doi.org/10.1177/1077699016639231. Cite
Sukhija, N., M. Tatineni, N. Brown, M. V. Moer, P. Rodriguez, and S. Callicott. “Topic Modeling and Visualization for Big Data in Social Sciences.” In 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 1198–1205, 2016. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0183. Cite
Buurma, Rachel Sagner. “The Fictionality of Topic Modeling: Machine Reading Anthony Trollope’s Barsetshire Series.” Big Data & Society 2, no. 2 (2015): 2053951715610591. https://doi.org/10.1177/2053951715610591. Cite
Goldstone, Andrew, Susana Galán, C. Laura Lovin, Andrew Mazzaschi, and Lindsay Whitmore, eds. “An Interactive Topic Model of Signs (Visualized in Dfr-Browser).” Signs, 2014. http://signsat40.signsjournal.org/topic-model/. Cite
Hoyt, Eric. “Lenses for Lantern: Data Mining, Visualization, and Excavating Film History’s Neglected Sources.” Film History 26, no. 2 (2014): 146–68. https://doi.org/10.2979/filmhistory.26.2.146. Cite
Goldstone, Andrew, and Ted Underwood. “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us.” New Literary History 45, no. 3 (2014): 359–84. https://doi.org/10.1353/nlh.2014.0025. Cite
Jockers, Matthew Lee. Macroanalysis: Digital Methods and Literary History. Topics in the Digital Humanities. Urbana: University of Illinois Press, 2013. Cite
Rhody, Lisa M. Topic Modeling and Figurative Language, 2013. http://journalofdigitalhumanities.org/2-1/topic-modeling-and-figurative-language-by-lisa-m-rhody/. Cite
Mohr, John W., and Petko Bogdanov. “Introduction—Topic Models: What They Are and Why They Matter.” Poetics, Topic Models and the Cultural Sciences, 41, no. 6 (2013): 545–69. https://doi.org/10.1016/j.poetic.2013.10.001. Cite
Jockers, Matthew L., and David Mimno. “Significant Themes in 19th-Century Literature.” Poetics, Topic Models and the Cultural Sciences, 41, no. 6 (2013): 750–69. https://doi.org/10.1016/j.poetic.2013.08.005. Cite
Gao, Wei, Peng Li, and Kareem Darwish. “Joint Topic Modeling for Event Summarization across News and Social Media Streams.” In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 1173–82. CIKM ’12. Maui, Hawaii, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2396761.2398417. Cite
Schmidt, Benjamin M. “When You Have a MALLET, Everything Looks like a Nail.” Sapping Attention (blog), 2012. http://sappingattention.blogspot.com/2012/11/when-you-have-mallet-everything-looks.html. Cite
Jockers, Matthew L. 500 Themes from a Corpus of 19th-Century Fiction, 2012. http://www.matthewjockers.net/macroanalysisbook/macro-themes/. Cite
Nelson, Robert K. Mining the Dispatch, 2012. http://dsl.richmond.edu/dispatch/pages/home. Cite
Underwood, Ted. “What Kinds of ‘Topics’ Does Topic Modeling Actually Produce?” The Stone and the Shell (blog), 2012. https://tedunderwood.com/2012/04/01/what-kinds-of-topics-does-topic-modeling-actually-produce/. Cite
Quinn, Kevin M., Burt L. Monroe, Michael Colaresi, Michael H. Crespin, and Dragomir R. Radev. “How to Analyze Political Attention with Minimal Assumptions and Costs.” American Journal of Political Science 54, no. 1 (2009): 209–28. https://doi.org/10.1111/j.1540-5907.2009.00427.x. Cite
Ramage, Daniel, David Hall, Ramesh Nallapati, and Christopher D. Manning. “Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-Labeled Corpora.” In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1, 248–56. EMNLP ’09. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. http://dl.acm.org/citation.cfm?id=1699510.1699543. Cite
Hall, David, Daniel Jurafsky, and Christopher D. Manning. “Studying the History of Ideas Using Topic Models.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 363–71. Association for Computational Linguistics, 2008. https://web.stanford.edu/ jurafsky/hallemnlp08.pdf. 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. https://doi.org/10.1109/ICCV.2007.4408965. Cite