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Proceedings of the National Academy of Sciences of the United States of America v.114 no.4, 2017년, pp.E457 - E465   SCI SCIE
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Content analysis of 150 years of British periodicals

Lansdall-Welfare, Thomas (Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom ); Sudhahar, Saatviga (Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom ); Thompson, James (Department of History, University of Bristol, Bristol BS8 1TB, United Kingdom ); Lewis, Justin (School of Journalism, Media and Cultural Studies, University of Cardiff, Cardiff CF10 3NB, United Kingdom ); Cristianini, Nello (Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom; );
  • 초록  

    Significance The use of large datasets has revolutionized the natural sciences and is widely believed to have the potential to do so with the social and human sciences. Many digitization efforts are underway, but the high-throughput methods of data production have not yet led to a comparable output in analysis. A notable exception has been the previous statistical analysis of the content of historical books, which started a debate about the limitations of using big data in this context. This study moves the debate forward using a large corpus of historical British newspapers and tools from artificial intelligence to extract macroscopic trends in history and culture, including gender bias, geographical focus, technology, and politics, along with accurate dates for specific events. Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books. The corpus spans 150 years and is formed by millions of articles, representing 14% of all British regional outlets of the period. Simple content analysis of this corpus allowed us to detect specific events, like wars, epidemics, coronations, or conclaves, with high accuracy, whereas the use of more refined techniques from artificial intelligence enabled us to move beyond counting words by detecting references to named entities. These techniques allowed us to observe both a systematic underrepresentation and a steady increase of women in the news during the 20th century and the change of geographic focus for various concepts. We also estimate the dates when electricity overtook steam and trains overtook horses as a means of transportation, both around the year 1900, along with observing other cultural transitions. We believe that these data-driven approaches can complement the traditional method of close reading in detecting trends of continuity and change in historical corpora.


  • 주제어

    artificial intelligence .   digital humanities .   computational history .   data science .   Culturomics.  

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