skip to main content
research-article

Computational historiography: Data mining in a century of classics journals

Published: 27 April 2012 Publication History

Abstract

More than a century of modern Classical scholarship has created a vast archive of journal publications that is now becoming available online. Most of this work currently receives little, if any, attention. The collection is too large to be read by any single person and mostly not of sufficient interest to warrant traditional close reading. This article presents computational methods for identifying patterns and testing hypotheses about Classics as a field. Such tools can help organize large collections, introduce younger scholars to the history of the field, and act as a “survey,” identifying anomalies that can be explored using more traditional methods.

References

[1]
Blei, D., Ng, A., and Jordan, M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022.
[2]
Blei, D. M. and Lafferty, J. D. 2007. A correlated topic model of science. Ann. Appl. Statist. 1, 1, 17--35.
[3]
Block, S. and Newman, D. 2011. What, where, when and sometimes why: Data mining twenty years of women's history abstracts. J. Women's History.
[4]
Borner, K., Chen, C., and Boyack, K. W. 2003. Visualizing knowledge domains. Ann. Rev. Inf. Sci. Technol., 179--255.
[5]
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inf. Sci. Technol. 41, 6, 391--407.
[6]
Garfield, E., Pudovkin, A., and Istomin, V. 2003. Why do we need algorithmic historiography? J. Amer. Soc. Inf. Sci. Technol. 54, 5, 400--412.
[7]
Hall, D., Jurafsky, D., and Manning, C. D. 2008. Studying the history of ideas using topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'08). 363--371.
[8]
Hofmann, T. 1999. Probilistic latent semantic analysis. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI'99).
[9]
Journal of Roman Studies. 1985. Editor's note. J. Roman Stud. 75, ix.
[10]
Klavans, R. and Boyack, K. W. 2006. Identifying a better measure of relatedness for mapping science. J. Amer. Soc. Inf. Sci. Technol. 57, 2, 251--263.
[11]
Klavans, R. and Boyack, K. W. 2009. Toward a consensus map of science. J. Amer. Soc. Inf. Sci. Technol. 60, 3, 455--476.
[12]
Mimno, D., Wallach, H., Naradowsky, J., Smith, D. A., and McCallum, A. 2009. Polylingual topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'09).
[13]
Moretti, F. 2005. Graphs, Maps, Trees: Abstract Models for a Literary History. Verso.
[14]
Newman, D. and Block, S. 2006. Probabilistic topic decomposition of an eighteenth century newspaper. J. Amer. Soc. Inf. Sci. Technol. 57, 6, 753-767.
[15]
Ponte, J. M. and Croft, W. B. 1998. A language modeling approach to information retrieval. In Proceedings of the Annual ACM SIGIR Conference on Research and Development in Information Retrieval.
[16]
Small, H. 1999. Visualizing science by citation mapping. J. Amer. Soc. Inf. Sci. Technol. 50, 9, 799--813.
[17]
Smith, R. R. R. 1990. Late roman philosopher portraits from aphrodisias. The J. Roman Stud. 80, 127--155.
[18]
Yao, L., Mimno, D., and McCallum, A. 2009. Efficient methods for topic model inference on streaming document collections. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'09).
[19]
Zhai, C. and Lafferty, J. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the Annual ACM SIGIR Conference on Research and Development in Information Retrieval.

Cited By

View all
  • (2024)Predictive Analytics - An IntroductionIntelligent Technologies for Automated Electronic Systems10.2174/9789815179514124010006(53-64)Online publication date: 4-Mar-2024
  • (2023)Mining misinformation discourse on social media within the ‘ideological square’Discourse & Society10.1177/0957926523121149035:3(329-344)Online publication date: 4-Dec-2023
  • (2023)The Intersections between Sociology and STS: A Big Data ApproachSociological Perspectives10.1177/07311214231167170(073112142311671)Online publication date: 11-May-2023
  • Show More Cited By

Index Terms

  1. Computational historiography: Data mining in a century of classics journals

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Journal on Computing and Cultural Heritage
    Journal on Computing and Cultural Heritage   Volume 5, Issue 1
    April 2012
    53 pages
    ISSN:1556-4673
    EISSN:1556-4711
    DOI:10.1145/2160165
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 April 2012
    Accepted: 01 August 2011
    Revised: 01 July 2011
    Received: 01 February 2011
    Published in JOCCH Volume 5, Issue 1

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)85
    • Downloads (Last 6 weeks)6
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Predictive Analytics - An IntroductionIntelligent Technologies for Automated Electronic Systems10.2174/9789815179514124010006(53-64)Online publication date: 4-Mar-2024
    • (2023)Mining misinformation discourse on social media within the ‘ideological square’Discourse & Society10.1177/0957926523121149035:3(329-344)Online publication date: 4-Dec-2023
    • (2023)The Intersections between Sociology and STS: A Big Data ApproachSociological Perspectives10.1177/07311214231167170(073112142311671)Online publication date: 11-May-2023
    • (2023)Twenty years of research on technology in mathematics education at CERME: a literature review based on a data science approachEducational Studies in Mathematics10.1007/s10649-022-10202-z112:2(309-336)Online publication date: 4-Jan-2023
    • (2021)Knight Moves: Russifying Quantitative Literary StudiesRussian Literature10.1016/j.ruslit.2021.07.006122-123(113-138)Online publication date: May-2021
    • (2021)IntroductionRussian Literature10.1016/j.ruslit.2021.07.001122-123(1-6)Online publication date: May-2021
    • (2021)Topic space trajectoriesScientometrics10.1007/s11192-021-03931-0126:7(5759-5795)Online publication date: 1-Jul-2021
    • (2021)Evaluating the Robustness of Embedding-Based Topic Models to OCR NoiseTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_30(392-400)Online publication date: 1-Dec-2021
    • (2021)Follow the leaderJournal of the Association for Information Science and Technology10.1002/asi.2442172:4(478-492)Online publication date: 8-Mar-2021
    • (2020)Nested variational autoencoder for topic modelling on microtexts with word vectorsExpert Systems10.1111/exsy.1263938:2Online publication date: 8-Oct-2020
    • Show More Cited By

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media