research-article

Computational historiography: Data mining in a century of classics journals

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David MimnoAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 5, Issue 1

Article No.: 3, Pages 1 - 19

https://doi.org/10.1145/2160165.2160168

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.

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Index Terms

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

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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

Copyright © 2012 ACM.

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Association for Computing Machinery

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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

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Cited By

Vijayarangam JKamalakannan SKarthikeyan T(2024)Predictive Analytics - An IntroductionIntelligent Technologies for Automated Electronic Systems10.2174/9789815179514124010006(53-64)Online publication date: 4-Mar-2024
https://doi.org/10.2174/9789815179514124010006
Hamdi S(2023)Mining misinformation discourse on social media within the ‘ideological square’Discourse & Society10.1177/0957926523121149035:3(329-344)Online publication date: 4-Dec-2023
https://doi.org/10.1177/09579265231211490
Amuchastegui MBirch KKaltenbrunner W(2023)The Intersections between Sociology and STS: A Big Data ApproachSociological Perspectives10.1177/07311214231167170(073112142311671)Online publication date: 11-May-2023
https://doi.org/10.1177/07311214231167170
Herfort JTamborg AMeier FAllsopp BMisfeldt M(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
https://doi.org/10.1007/s10649-022-10202-z
Bozovic MChenoweth CLassin JKirkpatrick T(2021)Knight Moves: Russifying Quantitative Literary StudiesRussian Literature10.1016/j.ruslit.2021.07.006122-123(113-138)Online publication date: May-2021
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Bozovic M(2021)IntroductionRussian Literature10.1016/j.ruslit.2021.07.001122-123(1-6)Online publication date: May-2021
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https://dl.acm.org/doi/10.1007/s11192-021-03931-0
Zosa EMutuvi SGranroth-Wilding MDoucet A(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
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Soni SLerman KEisenstein J(2021)Follow the leaderJournal of the Association for Information Science and Technology10.1002/asi.2442172:4(478-492)Online publication date: 8-Mar-2021
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Trinh TQuan TMai T(2020)Nested variational autoencoder for topic modelling on microtexts with word vectorsExpert Systems10.1111/exsy.1263938:2Online publication date: 8-Oct-2020
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