What, Where, When, and Sometimes Why: Data Mining Two Decades of Women’s History Abstracts

Block, Sharon; Newman, David J.

doi:10.1353/jowh.2011.0001

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Blei and Lafferty apply topic modeling to 100 years of articles from a single journal, Science [Blei and Lafferty 2007]. Block and Newman use topic analysis to explore 18th century newspapers [Newman and Block 2006] and academic articles in the field of women's history [Block and Newman 2011]. There is also substantial work in creating "maps" of science [Börner et al 2003;Boyack 2006, 2009;Small 1999].…”

Section: Related Workmentioning

confidence: 99%

Computational historiography

Mimno

2012

J. Comput. Cult. Herit.

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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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Section: Related Workmentioning

confidence: 99%

Computational historiography

Mimno

2012

J. Comput. Cult. Herit.

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“…As noted above, humanities scholars often simplify the pre‐processing stage by making use of existing datasets. In a specific example, historian Sharon Block and computer scientist David Newman have published research based upon data mining article abstracts from widely used databases [3]. Similarly, economic historian Jeremy Atack has discussed the possibilities for researchers available in the Bateman‐Foust sample, which is a database of agricultural and population census data from 1850 to 1880 first begun with machine‐readable punch cards in the late 1960s and expanded in the 1990s [4].…”

Section: Technical Components Of Data Miningmentioning

confidence: 99%

A brief introduction to data mining projects in the humanities

Hagood

2012

Bul. Am. Soc. Info. Sci. Tech.

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Imagine having the ability to search the entire canon of Western literature quickly and easily for the use of a specific metaphor, references to a particular place or instances of an exact sequence of words or phrases. Last year's publication in the journal Science of research using preliminary results from Google's book digitization project drew attention to the potential of such data mining for exploring a variety of fields in the social sciences and the humanities [1]. At its simplest, data mining is the process of extracting new knowledge (usually in terms of previously unknown patterns) from sets of data already in existence. For instance, Shakespeare scholars have used data mining techniques to identify patterns of word usage in his plays, the texts of which have already been digitized. Similarly, there is a long history of researchers making use of U.S. census data to identify demographic trends or correlations with other datasets. Data mining is inherently an exercise in quantitative analysis, the results of which are subject to qualitative analyses that link the newly discovered patterns back to particular, representative examples from the original set of data.In the humanities, data mining necessarily entails an interdisciplinary and collaborative practice because it combines tools, techniques and methodologies from computer science and the humanities. As a consequence, data mining is often associated with the term digital humanities, which includes using cutting edge technology both to present the results of research and to conduct the research itself. Data mining is one example of the latter, and at its best a data mining project involves active collaboration between humanities scholars and information professionals to design and carry out the research program. In addition, because data mining is a relatively recent practice, the research project is often as novel for the computer and information sciences as it is for the humanities. Therefore, data mining projects are driven as much by the information professional as the humanities scholar. C O N T E N T S N E X T P A G E > N E X T A R T I C L E > < P R E V I O U S P A G E Special SectionEDITOR'S SUMMARY Data mining offers the capability to view data in a new light, discovering associations and patterns not appreciated before. For the humanities domain, it exemplifies the interdisciplinary efforts of digital humanities. The technique provides answers and prompts further questions from new discoveries. Part of knowledge discovery in databases, data mining involves identifying relevant n-grams, classifying and reclassifying results, modeling the interdependence of variables and clustering results into meaningful subgroups. From designing research questions to determining how best to display and communicate results, the process requires collaboration between information professionals and humanities scholars. A selection of data mining projects illustrates how the technique is being applied for humanities research. Tools for data mining are readily avai...

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“…In this Website, she publishes mostly a short biography and the works of each author but not explicitly classified the themes or genre. However, there has been some research that has used computational tools to classify topics in extensive collections of journals and political texts (Riddel, 2012;Block, Newman, 2011). In the field of Dutch literature, there are several attempts have been made to explore the features of a literary movement using computational tools (Naber, Boot, 2019;Kao, Jurafsky, 2015).…”

Section: Introductionmentioning

confidence: 99%

Travel, Grief and Fight: Computational Tracing of Female Works in the Dutch East Indies Literature

Astari

2021

IRHS

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In the canon of the Dutch Indies literature, the works of female writers are sometimes overlooked. Mainly when it appears in the early period of the Dutch colonial period, the central issue and genre in most of the literary works are frequently written by male authors through a masculine colonial perspective. This paper explores the works of female authors in the period of the Dutch East Indies (1800-1942) using two computational tools. The aim is to examine the most common themes that are appeared in the female authors' works throughout this long period. Using two computational tools, Stylo and Antconc, this paper reveals genre closeness and the most common themes that are appeared in twelve Dutch East Indies female novels which are collected from dbnl.org. Based on the cluster analysis, this paper divides the novels into four different clusters. More over the computational analysis also shows the three main themes; exploration, disadvantages of being a repressed group, and fighting against the oppressed. The use of extensive digital data in this research gives an alternative for literary researchers to find an alternative way of doing their research during this pandemic time, which makes the physical visit to the library limited. It also gives an alternative in the literature field to combine this discipline with computational tools.KEYWORDS: Indies literature, female literature, computational literary studies, digital humanities.

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What, Where, When, and Sometimes Why: Data Mining Two Decades of Women’s History Abstracts

Cited by 10 publications

References 22 publications

Computational historiography

Computational historiography

A brief introduction to data mining projects in the humanities

Travel, Grief and Fight: Computational Tracing of Female Works in the Dutch East Indies Literature

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