(all)
Global Humanities | History of Humanities | Liberal Arts | Humanities and Higher Education | Humanities as Research Activity | Humanities Teaching & Curricula | Humanities and the Sciences | Medical Humanities | Public Humanities | Humanities Advocacy | Humanities and Social Groups | Value of Humanities | Humanities and Economic Value | Humanities Funding | Humanities Statistics | Humanities Surveys | "Crisis" of the Humanities
Humanities Organizations: Humanities Councils (U.S.) | Government Agencies | Foundations | Scholarly Associations
Humanities in: Africa | Asia (East) | Asia (South) | Australasia | Europe | Latin America | Middle East | North America: Canada - Mexico - United States | Scandinavia | United Kingdom
(all)
Lists of News Sources | Databases with News Archives | History of Journalism | Journalism Studies | Journalism Statistics | Journalism Organizations | Student Journalism | Data Journalism | Media Frames (analyzing & changing media narratives using "frame theory") | Media Bias | Fake News | Journalism and Minorities | Journalism and Women | Press Freedom | News & Social Media
(all)
Corpus Representativeness
Comparison paradigms for idea of a corpus: Archives as Paradigm | Canons as Paradigm | Editions as Paradigm | Corpus Linguistics as Paradigm
(all)
Artificial Intelligence | Big Data | Data Mining | Data Notebooks (Jupyter Notebooks) | Data Visualization (see also Topic Model Visualizations) | Hierarchical Clustering | Interpretability & Explainability (see also Topic Model Interpretation) | Mapping | Natural Language Processing | Network Analysis | Open Science | Reporting & Documentation Methods | Reproducibility | Sentiment Analysis | Social Media Analysis | Statistical Methods | Text Analysis (see also Topic Modeling) | Text Classification | Wikification | Word Embedding & Vector Semantics
Topic Modeling (all)
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
Searchable version of bibliography on Zotero site
For WE1S developers: Biblio style guide | Biblio collection form (suggest additions) | WE1S Bibliography Ontology Outline
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Smith, Gary, and Jay Cordes. The Phantom Pattern Problem: The Mirage of Big Data. First edition. Oxford ; New York, NY: Oxford University Press, 2020. Cite
Thylstrup, Nanna Bonde, ed. Uncertain Archives: Critical Keywords for Big Data. Cambridge, Massachusetts: The MIT Press, 2020. Cite
Shu, Kai, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. “FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media.” ArXiv:1809.01286 [Cs], 2019. http://arxiv.org/abs/1809.01286. Cite
Liu, Yang, and Yi-Fang Brook Wu. “Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks.” In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16826. Cite
Shao, Chengcheng, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. “The Spread of Low-Credibility Content by Social Bots.” Nature Communications 9, no. 1 (2018): 1–9. https://doi.org/10.1038/s41467-018-06930-7. Cite
Parikh, Shivam B., and Pradeep K. Atrey. “Media-Rich Fake News Detection: A Survey.” In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 436–41, 2018. https://doi.org/10.1109/MIPR.2018.00093. Cite
Wu, Liang, and Huan Liu. “Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate.” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 637–45. WSDM ’18. Marina Del Rey, CA, USA: Association for Computing Machinery, 2018. https://doi.org/10.1145/3159652.3159677. Cite
Bakir, Vian, and Andrew McStay. “Fake News and The Economy of Emotions.” Digital Journalism 6, no. 2 (2018): 154–75. https://doi.org/10.1080/21670811.2017.1345645. Cite
Shu, Kai, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. “Fake News Detection on Social Media: A Data Mining Perspective.” Association for Computing Machinery, 2017. https://doi.org/10.1145/3137597.3137600. Cite
Shu, Kai, Suhang Wang, and Huan Liu. “Exploiting Tri-Relationship for Fake News Detection.” undefined, 2017. https://www.semanticscholar.org/paper/Exploiting-Tri-Relationship-for-Fake-News-Detection-Shu-Wang/8fd1d13e18c5ef8b57296adab6543cb810c36d81. Cite
Granik, Mykhailo, and Volodymyr Mesyura. “Fake News Detection Using Naive Bayes Classifier.” In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 900–903, 2017. https://doi.org/10.1109/UKRCON.2017.8100379. Cite
Tacchini, Eugenio, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, and Luca de Alfaro. “Some Like It Hoax: Automated Fake News Detection in Social Networks.” ArXiv:1704.07506 [Cs], 2017. http://arxiv.org/abs/1704.07506. Cite
Ahmed, Hadeer, Issa Traore, and Sherif Saad. “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques.” In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, edited by Issa Traore, Isaac Woungang, and Ahmed Awad, 127–38. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-69155-8_9. Cite
Potthast, Martin, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. “A Stylometric Inquiry into Hyperpartisan and Fake News.” ArXiv:1702.05638 [Cs], 2017. http://arxiv.org/abs/1702.05638. Cite
Pérez-Rosas, Verónica, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. “Automatic Detection of Fake News.” ArXiv:1708.07104 [Cs], 2017. http://arxiv.org/abs/1708.07104. Cite
Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. “Exposure to Ideologically Diverse News and Opinion on Facebook.” Science 348, no. 6239 (2015): 1130–32. https://doi.org/10.1126/science.aaa1160. Cite
Muthiah, Sathappan, Bert Huang, Jaime Arredondo, David Mares, Lise Getoor, Graham Katz, and Naren Ramakrishnan. “Planned Protest Modeling in News and Social Media.” In Twenty-Seventh IAAI Conference, 2015. https://www.aaai.org/ocs/index.php/IAAI/IAAI15/paper/view/9652. Cite
Chen, Min, Shiwen Mao, and Yunhao Liu. “Big Data: A Survey.” Mobile Networks and Applications 19, no. 2 (2014): 171–209. https://doi.org/10.1007/s11036-013-0489-0. Cite
Tsytsarau, Mikalai, Themis Palpanas, and Malu Castellanos. “Dynamics of News Events and Social Media Reaction.” In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 901–10. KDD ’14. New York, New York, USA: Association for Computing Machinery, 2014. https://doi.org/10.1145/2623330.2623670. Cite
Sagiroglu, Seref, and Duygu Sinanc. “Big Data: A Review.” In 2013 International Conference on Collaboration Technologies and Systems (CTS), 42–47, 2013. https://doi.org/10.1109/CTS.2013.6567202. Cite
Xie, Lexing, Apostol Natsev, John R. Kender, Matthew Hill, and John R. Smith. “Visual Memes in Social Media: Tracking Real-World News in YouTube Videos.” In Proceedings of the 19th ACM International Conference on Multimedia, 53–62. MM ’11. Scottsdale, Arizona, USA: Association for Computing Machinery, 2011. https://doi.org/10.1145/2072298.2072307. Cite
Tsagkias, Manos, Maarten de Rijke, and Wouter Weerkamp. “Linking Online News and Social Media.” In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 565–74. WSDM ’11. Hong Kong, China: Association for Computing Machinery, 2011. https://doi.org/10.1145/1935826.1935906. Cite
Sculley, D., and B. M. Pasanek. “Meaning and Mining: The Impact of Implicit Assumptions in Data Mining for the Humanities.” Literary and Linguistic Computing 23, no. 4 (2008): 409–24. https://doi.org/10.1093/llc/fqn019. Cite