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The Actors of History: Narrative Network Analysis Reveals the Institutions of Power in British Society Between 1800-1950

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Advances in Intelligent Data Analysis XVI (IDA 2017)

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Abstract

In this study we analyze a corpus of 35.9 million articles from local British newspapers published between 1800 and 1950, investigating the changing role played by key actors in public life. This involves the role of institutions (such as the Church or Parliament) and individual actors (such as the Monarch). The analysis is performed by transforming the corpus into a narrative network, whose nodes are actors, whose links are actions, and whose communities represent tightly interacting parts of society. We observe how the relative importance of these communities evolves over time, as well as the centrality of various actors. All this provides an automated way to analyze how different actors and institutions shaped public discourse over a time span of 150 years. We discover the role of the Church, Monarchy, Local Government, and the peculiarities of the separation of powers in the United Kingdom. The combination of AI algorithms with tools from the computational social sciences and data-science, is a promising way to address the many open questions of Digital Humanities.

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Notes

  1. 1.

    The full list of networks, along with their properties can be seen at http://thinkbig.enm.bris.ac.uk/supp-info-actors-of-history/.

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Acknowledgments

Thomas Lansdall-Welfare, Saatviga Sudhahar and Nello Cristianini are supported by the ERC Advanced Grant “ThinkBig” awarded to NC. The authors would like to thank FindMyPast for making the original corpus available for study, as well as Dr. Gaetano Dato for his helpful comments.

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Correspondence to Nello Cristianini .

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Lansdall-Welfare, T., Sudhahar, S., Thompson, J., Cristianini, N. (2017). The Actors of History: Narrative Network Analysis Reveals the Institutions of Power in British Society Between 1800-1950. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-68765-0_16

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