Loading…

Economic history goes digital: topic modeling the Journal of Economic History

Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large coll...

Full description

Saved in:
Bibliographic Details
Published in:Cliometrica 2019-01, Vol.13 (1), p.83-125
Main Author: Wehrheim, Lino
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3
cites cdi_FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3
container_end_page 125
container_issue 1
container_start_page 83
container_title Cliometrica
container_volume 13
creator Wehrheim, Lino
description Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.
doi_str_mv 10.1007/s11698-018-0171-7
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2037261537</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2037261537</sourcerecordid><originalsourceid>FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3</originalsourceid><addsrcrecordid>eNp1kDFPwzAQhS0EEqXwA9gsMQd8dmwnbKgqFFTE0t1yHCdNlcTBTof-exIZlYnhdCe99z2dHkL3QB6BEPkUAESeJQTmkZDIC7SATLCEcmCX55vwa3QTwoEQwSZlgT7XxvWuawzeN2F0_oRrZwMum7oZdfuMRzdMWudK2zZ9jce9xR_u6HvdYlfhM7yJ8C26qnQb7N3vXqLd63q32iTbr7f31cs2MSlNx8SmFCSlRDKTgiB5aWReAQWeVtxoUWY85xa0KZhhGsBII4WUFgQXhSg0W6KHGDt49320YVSH-FNQlDBJBXAmJxdEl_EuBG8rNfim0_6kgKi5NBVLU1Npai5NzQyNTJi8fW39X_L_0A9YmW5O</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2037261537</pqid></control><display><type>article</type><title>Economic history goes digital: topic modeling the Journal of Economic History</title><source>ABI/INFORM Global</source><source>Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List</source><creator>Wehrheim, Lino</creator><creatorcontrib>Wehrheim, Lino</creatorcontrib><description>Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.</description><identifier>ISSN: 1863-2505</identifier><identifier>EISSN: 1863-2513</identifier><identifier>DOI: 10.1007/s11698-018-0171-7</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Data mining ; Digitization ; Econometrics ; Economic history ; Economic models ; Economic statistics ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Economics and Finance ; Finance ; History ; History of Economic Thought/Methodology ; Insurance ; Management ; Original Paper ; Statistics for Business</subject><ispartof>Cliometrica, 2019-01, Vol.13 (1), p.83-125</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Cliometrica is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3</citedby><cites>FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3</cites><orcidid>0000-0001-9269-8116</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2037261537/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2037261537?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Wehrheim, Lino</creatorcontrib><title>Economic history goes digital: topic modeling the Journal of Economic History</title><title>Cliometrica</title><addtitle>Cliometrica</addtitle><description>Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.</description><subject>Algorithms</subject><subject>Data mining</subject><subject>Digitization</subject><subject>Econometrics</subject><subject>Economic history</subject><subject>Economic models</subject><subject>Economic statistics</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Economics</subject><subject>Economics and Finance</subject><subject>Finance</subject><subject>History</subject><subject>History of Economic Thought/Methodology</subject><subject>Insurance</subject><subject>Management</subject><subject>Original Paper</subject><subject>Statistics for Business</subject><issn>1863-2505</issn><issn>1863-2513</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kDFPwzAQhS0EEqXwA9gsMQd8dmwnbKgqFFTE0t1yHCdNlcTBTof-exIZlYnhdCe99z2dHkL3QB6BEPkUAESeJQTmkZDIC7SATLCEcmCX55vwa3QTwoEQwSZlgT7XxvWuawzeN2F0_oRrZwMum7oZdfuMRzdMWudK2zZ9jce9xR_u6HvdYlfhM7yJ8C26qnQb7N3vXqLd63q32iTbr7f31cs2MSlNx8SmFCSlRDKTgiB5aWReAQWeVtxoUWY85xa0KZhhGsBII4WUFgQXhSg0W6KHGDt49320YVSH-FNQlDBJBXAmJxdEl_EuBG8rNfim0_6kgKi5NBVLU1Npai5NzQyNTJi8fW39X_L_0A9YmW5O</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Wehrheim, Lino</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQHSC</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9269-8116</orcidid></search><sort><creationdate>20190101</creationdate><title>Economic history goes digital: topic modeling the Journal of Economic History</title><author>Wehrheim, Lino</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Data mining</topic><topic>Digitization</topic><topic>Econometrics</topic><topic>Economic history</topic><topic>Economic models</topic><topic>Economic statistics</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Economics</topic><topic>Economics and Finance</topic><topic>Finance</topic><topic>History</topic><topic>History of Economic Thought/Methodology</topic><topic>Insurance</topic><topic>Management</topic><topic>Original Paper</topic><topic>Statistics for Business</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wehrheim, Lino</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>History Study Center</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Cliometrica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wehrheim, Lino</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Economic history goes digital: topic modeling the Journal of Economic History</atitle><jtitle>Cliometrica</jtitle><stitle>Cliometrica</stitle><date>2019-01-01</date><risdate>2019</risdate><volume>13</volume><issue>1</issue><spage>83</spage><epage>125</epage><pages>83-125</pages><issn>1863-2505</issn><eissn>1863-2513</eissn><abstract>Digitization and computer science have established a completely new set of methods with which to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, i.e., statistical algorithms that automatically infer the content from large collections of texts. In this article, I present an introduction to topic modeling and give an initial review of the research using topic models. I illustrate their capacity by applying them to 2675 articles published in the Journal of Economic History between 1941 and 2016. By comparing the results to traditional research on the JEH and to recent studies on the cliometric revolution, I aim to demonstrate how topic models can enrich economic historians’ methodological toolboxes.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11698-018-0171-7</doi><tpages>43</tpages><orcidid>https://orcid.org/0000-0001-9269-8116</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1863-2505
ispartof Cliometrica, 2019-01, Vol.13 (1), p.83-125
issn 1863-2505
1863-2513
language eng
recordid cdi_proquest_journals_2037261537
source ABI/INFORM Global; Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List
subjects Algorithms
Data mining
Digitization
Econometrics
Economic history
Economic models
Economic statistics
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Finance
History
History of Economic Thought/Methodology
Insurance
Management
Original Paper
Statistics for Business
title Economic history goes digital: topic modeling the Journal of Economic History
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T01%3A27%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Economic%20history%20goes%20digital:%20topic%20modeling%20the%20Journal%20of%20Economic%20History&rft.jtitle=Cliometrica&rft.au=Wehrheim,%20Lino&rft.date=2019-01-01&rft.volume=13&rft.issue=1&rft.spage=83&rft.epage=125&rft.pages=83-125&rft.issn=1863-2505&rft.eissn=1863-2513&rft_id=info:doi/10.1007/s11698-018-0171-7&rft_dat=%3Cproquest_cross%3E2037261537%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c424t-e421722073c41609dc79f12154f5ca6d8595e1acb3c3a11c7c7677e1656b6ba3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2037261537&rft_id=info:pmid/&rfr_iscdi=true