Studying how the past is remembered: towards computational history through large scale text mining

  • Authors:

  • Ching-man Au Yeung;Adam Jatowt

  • Affiliations:

  • ASTRI, Hong Kong, Hong Kong;Kyoto University, Kyoto, Japan

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

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Abstract

History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societal views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support.