Skip to main content
Article
Licensed
Unlicensed Requires Authentication

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

  • ORCID logo and ORCID logo EMAIL logo
Published/Copyright: September 30, 2022
Libri
From the journal Libri Volume 73 Issue 1

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.

Keywords: China; news topics; The Economist; the UK; the US; topic modeling
Corresponding author: Ganzhou Zhang, Qianjiang College, Hangzhou Normal University, 16 Xuelin Street, Xiasha, 310018 Hangzhou, China, E-mail:

  1. Competing interests: All the authors state that there is no conflict of interest. All the authors have no relevant financial or non-financial interests to disclose.

References

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.10.1145/290941.290954Search in Google Scholar

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.10.1007/978-3-642-13657-3_43Search in Google Scholar

Asuncion, A., M. Welling, P. Smyth, and Y. W. Teh. 2009. “On smoothing and inference for topic models.” In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, 27–34. AUAI Press.Search in Google Scholar

Audit Bureau of Circulations Report. 2018. The Economist – 2018 Worldwide Brand Report. Audit Bureau of Circulations. https://www.abc.org.uk/Certificates/49370140.pdf (accessed March 5, 2019).Search in Google Scholar

Bell, A. 1991. The language of news media. Oxford, UK: Blackwell.Search in Google Scholar

Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent dirichlet allocation.” Journal of Machine Learning Research 3: 993–1022.Search in Google Scholar

Bolelli, L., Ş. Ertekin, and C. L. Giles. 2009. “Topic and trend detection in text collections using latent dirichlet allocation.” In European Conference on Information Retrieval, 776–780. Berlin, Heidelberg: Springer.10.1007/978-3-642-00958-7_84Search in Google Scholar

Bonilla, T., and J. Grimmer. 2013. “Elevated threat levels and decreased expectations: How democracy handles terrorist threats.” Poetics 41: 650–669. https://doi.org/10.1016/j.poetic.2013.06.003.Search in Google Scholar

Brewer, M. B. 1979. “In-group bias in the minimal intergroup situation: A cognitive-motivational analysis.” Psychological Bulletin 86: 307–324. https://doi.org/10.1037/0033-2909.86.2.307.Search in Google Scholar

Brewer, M. B. 1999. “The psychology of prejudice: Ingroup love or outgroup hate?” Journal of Social Issues 55: 429–444. https://doi.org/10.1111/0022-4537.00126.Search in Google Scholar

Brown, G., and G. Yule. 1983. Discourse analysis. Cambridge, UK: Cambridge University Press.10.1017/CBO9780511805226Search in Google Scholar

Cieri, C., D. Graff, M. Liberman, N. Martey, and S. M. Strassel. 2000. Large, Multilingual, Broadcast News Corpora for Cooperative Research in Topic Detection and Tracking: The TDT-2 and TDT-3 Corpus Efforts, 925–930. In Proceedings of The Second International Conference on Language Resources and Evaluation, May 31–June 2, 2000.Search in Google Scholar

De Battisti, F., A. Ferrara, and S. Salini. 2015. “A decade of research in statistics: a topic model approach.” Scientometrics 103 (2): 413–433. https://doi.org/10.1007/s11192-015-1554-1.Search in Google Scholar

Der-Karabetian, A., and N. Balian, N. 1992. “Ingroup, outgroup, and global-human identities of Turkish-Armenians.” The Journal of Social Psychology 132 (4): 497–504. https://doi.org/10.1080/00224545.1992.9924729.Search in Google Scholar

DiMaggio, P., M. Nag, and D. M. Blei. 2013. “Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding.” Poetics 41: 570–606. https://doi.org/10.1016/j.poetic.2013.08.004.Search in Google Scholar

Edelstein, A. S. 1993. “Thinking about the criterion variable in agenda-setting research.” Journal of Communication 43 (2): 85–99. https://doi.org/10.1111/j.1460-2466.1993.tb01264.x.Search in Google Scholar

Editor’s note. 2013. The Economist explains itself – Why are The Economist’s writers anonymous? https://www.economist.com/the-economist-explains/2013/09/04/why-are-the-economists-writers-anonymous (accessed March 18, 2019).Search in Google Scholar

Elgesem, D., I. Feinerer, and L. Steskal. 2016. “Bloggers’ responses to the Snowden affair: Combining automated and manual methods in the analysis of news blogging.” Computer Supported Cooperative Work 25:167–191. https://doi.org/10.1007/s10606-016-9251-z.Search in Google Scholar

Evans, M. S. 2014. “A computational approach to qualitative analysis in large textual datasets.” PLoS One 9 (2): 1–10. https://doi.org/10.1371/journal.pone.0087908.Search in Google Scholar

Fiscus, J. G., and G. R. Doddington. 2002. “Topic detection and tracking evaluation overview.” In Topic detection and tracking, 17–31. Boston, MA: Springer. https://pdfs.semanticscholar.org/f753/eaae780e5731d29ef4fbce02e58584c39792.pdf.10.1007/978-1-4615-0933-2_2Search in Google Scholar

Fowler, R. 2013. Language in the News: Discourse and Ideology in the Press. Routledge.10.4324/9781315002057Search in Google Scholar

Ghosh, D. D., and R. Guha. 2013. “What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and Geographic Information System.” Cartography and Geographic Information Science 40 (2): 90–102. https://doi.org/10.1080/15230406.2013.776210.Search in Google Scholar

Griffiths, T. L., and M. Steyvers. 2004. “Finding scientific topics.” Proceedings of the National Academy of Sciences 101 (suppl 1): 5228–5235. https://doi.org/10.1073/pnas.0307752101.Search in Google Scholar

Günther, E., and E. Domahidi. 2017. “What communication scholars write about: An analysis of 80 years of research in high-impact journals.” International Journal of Communication 11: 21.Search in Google Scholar

Guo, L., C. J. Vargo, Z. Pan, W. Ding, and P. Ishwar. 2016. “Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling.” Journalism & Mass Communication Quarterly: 93 (2): 332–359. https://doi.org/10.1177/1077699016639231.Search in Google Scholar

Jacobi, C., W. Van Atteveldt, and K. Welbers. 2016. “Quantitative analysis of large amounts of journalistic texts using topic modelling.” Digital Journalism 4 (1): 89–106. https://doi.org/10.1080/21670811.2015.1093271.Search in Google Scholar

Koltsova, O., and S. Koltcov. 2013. “Mapping the public agenda with topic modeling: The case of the Russian LiveJournal.” Policy & Internet 5 (2): 207–227. https://doi.org/10.1002/1944-2866.poi331.Search in Google Scholar

Koltsova, O., and A. Shcherbak. 2015. “‘LiveJournal Libra!’: The political blogosphere and voting preferences in Russia in 2011–2012.” New Media & Society 17 (10): 1715–1732. https://doi.org/10.1177/1461444814531875.Search in Google Scholar

Maier, D., A. Waldherr, P. Miltner, P. Jähnichen, and B. Pfetsch. 2018. “Exploring issues in a networked public sphere: Combining hyperlink network analysis and topic modeling.” Social Science Computer Review 36 (1): 3–20. https://doi.org/10.1177/0894439317690337.Search in Google Scholar

McCallum, A. K. 2002. MALLET: A Machine Learning for Language Toolkit. MALLET website. http://mallet.cs.umass.edu (accessed January 9, 2019).Search in Google Scholar

Nallapati, R., and J. Allan. 2002. “Capturing term dependencies using a language model based on sentence trees.” In Proceedings of the Eleventh International Conference on Information and Knowledge Management, 383–390. ACM.10.1145/584792.584855Search in Google Scholar

Newman, D., C. Chemudugunta, P. Smyth, and M. Steyvers. 2006. “Analyzing entities and topics in news articles using statistical topic models.” In Intelligence and Security Informatics, edited by S. Mehrotra, D. D. Zeng, H. Chen, B. Thuraisingham, and F.-Y. Wang, Vol. 3975, 93–104. Berlin, Germany: Springer.10.1007/11760146_9Search in Google Scholar

Nikita, M. 2019. R Package of Tuning of the Latent Dirichlet Allocation Models Parameters. The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/ldatuning/index.html (accessed March 10, 2019).Search in Google Scholar

Nikolenko, S. I., S. Koltcov, and O. Koltsova. 2017. “Topic modelling for qualitative studies.” Journal of Information Science 43 (1): 88–102. https://doi.org/10.1177/0165551515617393.Search in Google Scholar

Parra, D., C. Trattner, D. Gómez, M. Hurtado, X. Wen, and Y. Lin. 2016. “Twitter in academic events: a study of temporal usage, communication, sentimental and topical patterns in 16 computer science conferences.” Computer Communications 73: 301–314.10.1016/j.comcom.2015.07.001Search in Google Scholar

Piepenbrink, A., and E. Nurmammadov. 2015. “Topics in the literature of transition economies and emerging markets.” Scientometrics 102 (3): 2107–2130. https://doi.org/10.1007/s11192-014-1513-2.Search in Google Scholar

Proquest Business Premium Collection (PBPC). 2018. https://www.proquest.com/products-services/Business-Premium-Collection.html (accessed October 9, 2018).Search in Google Scholar

Pruteanu-Malinici, I., L. Ren, J. Paisley, E. Wang, and L. Carin. 2010. “Hierarchical Bayesian modeling of topics in time-stamped documents.” IEEE transactions on pattern analysis and machine intelligence 32 (6): 996–1011. https://doi.org/10.1109/tpami.2009.125.Search in Google Scholar

Puschmann, C., and T. Scheffler. 2016. “Topic modeling for media and communication research: A short primer.” HIIG Discussion Paper Series (No. 2016-05). Alexander von Humboldt Institut für Internet und Gesellschaft.10.2139/ssrn.2836478Search in Google Scholar

Qi, Y., N. Zhu, Y. Zhai, and Y. Ding. 2018. “The mutually beneficial relationship of patents and scientific literature: topic evolution in nanoscience.” Scientometrics 115 (2): 893–911. https://doi.org/10.1007/s11192-018-2693-y.Search in Google Scholar

Rauchfleisch, A. 2017. “The public sphere as an essentially contested concept: A co-citation analysis of the last 20 years of public sphere research.” Communication and the Public 2 (1): 3–18. https://doi.org/10.1177/2057047317691054.Search in Google Scholar

Savoy, J. 2013. “Authorship attribution based on a probabilistic topic model.” Information Processing & Management 49 (1): 341–354. https://doi.org/10.1016/j.ipm.2012.06.003.Search in Google Scholar

Schinas, M., S. Papadopoulos, Y. Kompatsiaris, and P. A. Mitkas. 2015. “Visual event summarization on social media using topic modelling and graph-based ranking algorithms.” In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 203–210. ACM.10.1145/2671188.2749407Search in Google Scholar

Smiler, A. P. 2006. “Living the image: A quantitative approach to delineating masculinities.” Sex Roles 55 (9–10): 621–632. https://doi.org/10.1007/s11199-006-9118-8.Search in Google Scholar

Stevens, K., P. Kegelmeyer, D. Andrzejewski, and D. Buttler. 2012. “Exploring topic coherence over many models and many topics.” In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 952–961. Association for Computational Linguistics.Search in Google Scholar

Sugimoto, C. R., D. Li, T. G. Russell, S. C. Finlay, and Y. Ding. 2011. “The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation.” Journal of the American Society for Information Science and Technology 62 (1): 185–204. https://doi.org/10.1002/asi.21435.Search in Google Scholar

Van Dijk, T. A. 2006. “Ideology and discourse analysis.” Journal of political ideologies 11 (2): 115–140. https://doi.org/10.1080/13569310600687908.Search in Google Scholar

Van Dijk, T. A. 2009. “News, Discourse, and Ideology.” In The Handbook of Journalism Studies, edited by K. Wahl-Jorgensen, and T. Hanitzsch, 191–204. New York: Routledge.Search in Google Scholar

Van Dijk, T. A. 2015. Racism and the Press. Routledge.10.4324/9781315682662Search in Google Scholar

Wallach, H. M., D. M. Mimno, and A. McCallum. 2009. “Rethinking LDA: Why priors matter.” In Proceedings of Neural Information Processing Systems, 1973–1981.Search in Google Scholar

Wang, H., Y. Ding, J. Tang, X. Dong, B. He, J. Qiu, and D. J. Wild. 2011. “Finding complex biological relationships in recent PubMed articles using Bio-LDA.” PloS one 6 (3): e17243. https://doi.org/10.1371/journal.pone.0017243.Search in Google Scholar

Wayne, C. L. 2000. “Topic detection and tracking in English and Chinese.” In Proceedings of the Fifth International Workshop on Information Retrieval With Asian Languages, 165–172. ACM.10.1145/355214.355238Search in Google Scholar

Wehrheim, L. 2019. “Economic history goes digital: topic modeling the Journal of Economic History.” Cliometrica 13 (1): 83–125. https://doi.org/10.1007/s11698-018-0171-7.Search in Google Scholar

Wei, X., and W. B. Croft. 2006. “LDA-based document models for ad-hoc retrieval.” In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 178–185. ACM.10.1145/1148170.1148204Search in Google Scholar

World Bank Report. 2019. The World Bank In China – Overview. World Bank. https://www.worldbank.org/en/country/china/overview#1 (accessed September 2, 2019).10.1596/978-1-4648-1328-3_ovSearch in Google Scholar

Wu, Q., C. Zhang, and X. An. 2013. “Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field.” Journal of Information Science 39 (3): 319–332. https://doi.org/10.1177/0165551512457893.Search in Google Scholar

Zhao, W. X., J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, and X. Li. 2011. “Comparing twitter and traditional media using topic models.” In European conference on information retrieval, 338–349. Berlin, Heidelberg: Springer.10.1007/978-3-642-20161-5_34Search in Google Scholar
Published Online: 2022-09-30
Published in Print: 2023-03-28

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Alethic Rights and Alethic Pluralism in Libraries
  3. Digital Archiving Policies of Central European Journals Registered in the Directory of Open Access Journals
  4. The Information Seeking Behaviors and Needs of Suicide Survivors During Bereavement
  5. Comparisons of the Economist Topics on Three Countries from 1991 Through 2016
  6. A Comparative Study of Information Literacy Study Trends Between Korea and Other Countries Using Keyword Networks
  7. “We Collect TONS of Data … We Report What We Think Our Community Cares the Most About … We Learn so Much from It:” School Librarians’ Evidence Collection and Sharing Practices
  8. Sex in the Stacks: Examining the Treatment of Explicit Materials in American Libraries
  9. A Study of Academic Library through the Prism of Knowledge Use and Problem Solving Networks
https://doi.org/10.1515/libri-2022-0026
Keywords for this article
China; news topics; The Economist; the UK; the US; topic modeling

Articles in the same Issue

  1. Frontmatter
  2. Alethic Rights and Alethic Pluralism in Libraries
  3. Digital Archiving Policies of Central European Journals Registered in the Directory of Open Access Journals
  4. The Information Seeking Behaviors and Needs of Suicide Survivors During Bereavement
  5. Comparisons of the Economist Topics on Three Countries from 1991 Through 2016
  6. A Comparative Study of Information Literacy Study Trends Between Korea and Other Countries Using Keyword Networks
  7. “We Collect TONS of Data … We Report What We Think Our Community Cares the Most About … We Learn so Much from It:” School Librarians’ Evidence Collection and Sharing Practices
  8. Sex in the Stacks: Examining the Treatment of Explicit Materials in American Libraries
  9. A Study of Academic Library through the Prism of Knowledge Use and Problem Solving Networks
Downloaded on 20.4.2026 from https://www.degruyterbrill.com/document/doi/10.1515/libri-2022-0026/html
Scroll to top button