Bibliography – Data Mining

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


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, 637–645. 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, 901–910. 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, 565–574. 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): 409–424. https://doi.org/10.1093/llc/fqn019. Cite