Comparisons of the Economist Topics on Three Countries from 1991 Through 2016

Author:

Guo Shesen1ORCID,Zhang Ganzhou1ORCID

Affiliation:

1. Qianjiang College , Hangzhou Normal University , 16 Xuelin Street, Xiasha , 310018 Hangzhou , China

Abstract

Abstract New topic modeling technique has been increasingly used in research of communication for quick discovery of latent topics that are spread across huge volumes of text. This work intends to analyze and compare the topics automatically generated by Latent Dirichlet Allocation (LDA). The data for building LDA model in this work is based on 38,124 articles published from 1991 through 2016 in one of the world’s most influential political and economic magazines, The Economist. The retrieved documents for generating topics are divided into three countries of the UK, the US, and China in order to observe topical differences between these ingroup or outgroup countries in The Economist coverage. The work analyzes interpretability, overall weight distributions, and historical changing patterns of the topics using LDA model diagnostics. It discusses the hot or increasing trends using regression coefficient. The work also tentatively explores the relationship between the media agenda and events.

Publisher

Walter de Gruyter GmbH

Subject

Library and Information Sciences

Reference60 articles.

1. Aberson, C. L., M. Healy, and V. Romero. 2000. “Ingroup bias and self-esteem: A meta-analysis.” Personality and Social Psychology Review 4: 157–173. https://doi.org/10.1207/s15327957pspr0402_04.

2. Aletras, N., and M. Stevenson. 2013. “Representing topics using images.” In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 158–167.

3. Allan, J., J. G. Carbonell, G. Doddington, J. Yamron, and Y. Yang. 1998. “Topic detection and tracking pilot study final report.” In Proceedings of DARPA Broadcast News Transcription and Understanding Workshop, 94–218. Lansdowne, VA. http://maroo.cs.umass.edu/pdf/IR-137.pdf.

4. Allan, J., R. Papka, and V. Lavrenko. 1998. “Online new event detection and tracking.” In Proceedings of SIGIR ’98 Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 37–45. August 24–28, 1998. Melbourne, Australia.

5. Arun, R., V. Suresh, C. V. Madhavan, and M. N. Murthy. 2010. “On finding the natural number of topics with latent dirichlet allocation: Some observations.” In Pacific-Asia conference on knowledge discovery and data mining, 391–402. Berlin, Heidelberg: Springer.

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