Studying How the Past is Remembered: Towards Computational History Through Large Scale Text Mining
C. Au Yeung, and A. Jatowt. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, page 1231--1240. New York, NY, USA, ACM, (2011)
DOI: 10.1145/2063576.2063755
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.
%0 Conference Paper
%1 auyeung2011studying
%A Au Yeung, Ching-man
%A Jatowt, Adam
%B Proceedings of the 20th ACM International Conference on Information and Knowledge Management
%C New York, NY, USA
%D 2011
%I ACM
%K history mining text time
%P 1231--1240
%R 10.1145/2063576.2063755
%T Studying How the Past is Remembered: Towards Computational History Through Large Scale Text Mining
%U http://doi.acm.org/10.1145/2063576.2063755
%X 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.
%@ 978-1-4503-0717-8
@inproceedings{auyeung2011studying,
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.},
acmid = {2063755},
added-at = {2015-12-09T12:10:09.000+0100},
address = {New York, NY, USA},
author = {Au Yeung, Ching-man and Jatowt, Adam},
biburl = {https://www.bibsonomy.org/bibtex/2183a209bccf08b0b99a6e199e563ef0b/jaeschke},
booktitle = {Proceedings of the 20th ACM International Conference on Information and Knowledge Management},
doi = {10.1145/2063576.2063755},
interhash = {48e7b12664819fe2fa263fce0c7565e7},
intrahash = {183a209bccf08b0b99a6e199e563ef0b},
isbn = {978-1-4503-0717-8},
keywords = {history mining text time},
location = {Glasgow, Scotland, UK},
numpages = {10},
pages = {1231--1240},
publisher = {ACM},
series = {CIKM '11},
timestamp = {2015-12-09T12:10:09.000+0100},
title = {Studying How the Past is Remembered: Towards Computational History Through Large Scale Text Mining},
url = {http://doi.acm.org/10.1145/2063576.2063755},
year = 2011
}