Argamon is best known for his work on computational stylistics, particularly author profiling. Together with Moshe Koppel and others, he has shown how statistical analysis of word usage can determine an author's age, sex, native language, and personality type with high accuracy in English-language texts.[10][11][12] His work also has shown how textual features indicating differences between male and female authorship are consistent between languages and across time.[13][14][15]
Argamon also developed computational stylistic methods that provide insights into the meaning of stylistic differences. One of his key innovations for this purpose is the development of computational stylistic analysis using systemic functional linguistics.[16][17] For example, together with Jeff Dodick and Paul Chase, he examined whether there are clear and consistent differences between scientific method in experimental sciences and historical sciences. Their work showed how using systemic functional features in computational stylistic analysis provides evidence for multiple scientific methodologies of the sorts posited previously by philosophers of science.[18]
Linguistics for cybersecurity
Argamon has pushed for the increased use of linguistic analysis for attribution of cybersecurity attacks. He has pointed out how linguistic attribution techniques can be used to good effect on natural language texts that arise in different attack scenarios, and has provided analyses for high-profile cases such as the Sony Pictures hack,[19][20] the Democratic National Committee cyber attacks,[21] and the Shadow BrokersNSA leak.[22][23]
Data science
In 2013, Argamon founded the Illinois Institute of Technology Master of Data Science program,[24] which he directed until 2019. The program seeks to teach students "to think about the real problems that need to be solved, not to simply find technical solutions." Argamon views data scientists as "sensemakers", whose job is not merely to produce analytic results, but to help their clients make sense of a complex, uncertain, and fast-changing world through rigorous analysis and explanation of the data.[25][26]
^Kenneth Bloom, Navendu Garg, and Shlomo Argamon. Extracting appraisal expressions. In Proc. Human Language Technologies: Conference of the North American Association for Computational Linguistics (NAACL-HLT), Rochester, New York, April, 2007.
^Casey Whitelaw, Navendu Garg, and Shlomo Argamon. Using appraisal groups for sentiment analysis. In Proc. Conference on Information and Knowledge Management, Bremen, Germany, November 2005.
^Shlomo Argamon, Ken Bloom, Andrea Esuli, and Fabrizio Sebastiani. Automatically Determining Attitude Type and Force for Sentiment Analysis. 3rd Language and Technology Conference, Poznan, Poland, October 2007.
^Lisa Gandy, Nadji Allan, Mark Atallah, Ophir Frieder, Newton Howard, Sergey Kanareykin, Moshe Koppel, Mark Last, Yair Neuman, Shlomo Argamon. Automatic identification of conceptual metaphors with limited knowledge. In Proc. Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), Bellevue, WA, July 2013.
^Shlomo Argamon-Engelson and Ido Dagan. Committee-based sample selection for probabilistic classifiers. Journal of Artificial Intelligence Research, 11:335-360, 1999.
^Julio Ortega, Moshe Koppel, and Shlomo Argamon-Engelson. Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems, 3(4):470–490, 2001.
^Argamon, Shlomo, Moshe Koppel, Jonathan Fine, and Anat Rachel Shimoni. "Gender, genre, and writing style in formal written texts." Text 23, no. 3 (2003): 321-346.
^Argamon, Shlomo, Moshe Koppel, James W. Pennebaker, and Jonathan Schler. "Automatically profiling the author of an anonymous text." Communications of the ACM 52, no. 2 (2009): 119-123.
^Argamon, Shlomo, Jean-Baptiste Goulain, Russell Horton, and Mark Olsen. "Vive la Différence! Text mining gender difference in French literature." Digital Humanities Quarterly 3, no. 2 (2009).
^Argamon, Shlomo, Russell Horton, Mark Olsen, and Sterling Stuart Stein. "Gender, Race, and Nationality in BlackDrama, 1850-2000: Mining Differences in Language Use in Authors and their Characters." Proceedings of Digital Humanities (2007).
^Hota, Sobhan R., Shlomo Argamon, and Rebecca Chung. "Gender in Shakespeare: Automatic stylistics gender character classification using syntactic, lexical and lemma features." Proc. Chicago Colloquium on Digital Humanities and Computer Science (DHCS) (2006).
^Argamon, Shlomo, Casey Whitelaw, Paul Chase, Sobhan Raj Hota, Navendu Garg, and Shlomo Levitan. "Stylistic text classification using functional lexical features." Journal of the American Society for Information Science and Technology 58, no. 6 (2007): 802-822.
^Argamon, Shlomo, and Moshe Koppel. "The rest of the story: Finding meaning in stylistic variation." In The Structure of Style, pp. 79-112. Springer, Berlin, Heidelberg, 2010.
^Argamon, Shlomo, Jeff Dodick, and Paul Chase. "Language use reflects scientific methodology: A corpus-based study of peer-reviewed journal articles." Scientometrics 75, no. 2 (2008): 203-238.
