Culturomics

Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts.[1][2] Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage.[3] The term is an American neologism first described in a 2010 Science article called Quantitative Analysis of Culture Using Millions of Digitized Books, co-authored by Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden.[4]

Michel and Aiden helped create the Google Labs project Google Ngram Viewer which uses n-grams to analyze the Google Books digital library for cultural patterns in language use over time.

Studies

In a study called Culturomics 2.0, Kalev H. Leetaru examined news archives including print and broadcast media (television and radio transcripts) for words that imparted tone or "mood" as well as geographic data.[5][6] The research was able to retroactively predict the 2011 Arab Spring and successfully estimate the final location of Osama Bin Laden to within 124 miles.[5][6]

In a 2012 paper by Alexander M. Petersen and co-authors,[7] they found a "dramatic shift in the birth rate and death rates of words":[8] Deaths have increased and births have slowed. The authors also identified a universal "tipping point" in the life cycle of new words at about 30 to 50 years after their origin, they either enter the long-term lexicon or fall into disuse.[8]

In a 2014 paper by S. Roth, culturomic analyses is used to trace the decline of religion, the rise of politics, and the relevance of the economy to modern societies, with one of the major results being that modern societies do not appear to be capitalist or economized.[9] This paper is likely to be the first application of culturomics in sociology.

Culturomic approaches have been taken in the analysis of newspaper content in a number of studies by I. Flaounas and co-authors. These studies showed macroscopic trends across different news outlets and countries. In 2012, a study of 2.5 million articles suggested that gender bias in news coverage depends on topic and how the readability of newspaper articles is related to topic.[10] A separate study by the same researchers, covering 1.3 million articles from 27 countries,[11] showed macroscopic patterns in the choice of stories to cover. In particular, countries made similar choices when they were related by economic, geographical and cultural links. The cultural links were revealed by the similarity in voting for the Eurovision song contest. This study was performed on a vast scale, by using statistical machine translation, text categorisation and information extraction techniques.

The possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated in a study by T Lansdall-Welfare and co-authors.[12] The study considered 84 million tweets generated by more than 9.8 million users from the United Kingdom over a period of 31 months, showing how public sentiment in the UK has changed with the announcement of spending cuts.

In a 2013 study by S Sudhahar and co-authors, the automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.[13]

Narrative network of US Elections 2012[14]

In a 2014 study by S Sudhahar, T Lansdall-Welfare and co-authors, 5 million news articles were collected over 5 years[15] and then analyzed to suggest a significant shift in sentiment relative to coverage of nuclear power, corresponding with the disaster of Fukushima. The study also extracted concepts that were associated with nuclear power before and after the disaster, explaining the change in sentiment with a change in narrative framing.

Criticism

Linguists and lexicographers have expressed skepticism regarding the methods and results of some of these studies, including one by Petersen et al.[16]

References

  1. ↑ Cohen, Patricia (16 December 2010). "In 500 Billion Words, New Window on Culture". New York Times.
  2. ↑ Hayes, Brian (May–June 2011). "Bit Lit". American Scientist. 99 (3): 190. doi:10.1511/2011.90.190.
  3. ↑ Letcher, David W. (April 6, 2011). "Cultoromics: A New Way to See Temporal Changes in the Prevalence of Words and Phrases" (PDF). American Institute of Higher Education 6th International Conference Proceedings. 4 (1): 228.
  4. ↑ Michel, Jean-Baptiste; Liberman Aiden, Erez (16 December 2010). "Quantitative Analysis of Culture Using Millions of Digitized Books". Science. 331 (6014): 176–82. doi:10.1126/science.1199644. PMC 3279742Freely accessible. PMID 21163965.
  5. 1 2 Leetaru, Kalev H. (5 September 2011). "Culturomics 2.0: Forecasting Large-Scale Human Behavior Using Global News Media Tone In Time And Space". First Monday. 16 (9). doi:10.5210/fm.v16i9.3663.
  6. 1 2 Quick, Darren (7 September 2011). "Culturomics research uses quarter-century of media coverage to forecast human behavior". Gizmag.com. Retrieved 9 September 2011.
  7. ↑ Petersen, Alexander M. (15 March 2012). "Statistical Laws Governing Fluctuations in Word Use from Word Birth to Word Death". Scientific Reports. 2. doi:10.1038/srep00313.
  8. 1 2 "The New Science of the Birth and Death of Words ", CHRISTOPHER SHEA, Wall Street Journal, March 16, 2012
  9. ↑ Roth, S. (2014), "Fashionable functions. A Google ngram view of trends in functional differentiation (1800-2000)", International Journal of Technology and Human Interaction, Band 10, Nr. 2, S. 34-58 (online: http://ssrn.com/abstract=2491422).
  10. ↑ I. Flaounas, O. Ali, T. Lansdall-Welfare, T. De Bie, N. Mosdell, J. Lewis, N. Cristianini, RESEARCH METHODS IN THE AGE OF DIGITAL JOURNALISM, Digital Journalism, Routledge, 2012
  11. ↑ I. Flaounas, M. Turchi, O. Ali, N. Fyson, T. De Bie, N. Mosdell, J. Lewis, N. Cristianini, The Structure of EU Mediasphere, PLoS ONE, Vol. 5(12), pp. e14243, 2010.
  12. ↑ Effects of the Recession on Public Mood in the UK; T Lansdall-Welfare, V Lampos, N Cristianini; Mining Social Network Dynamics (MSND) session on Social Media Applications
  13. ↑ Network analysis of narrative content in large corpora; S Sudhahar, G De Fazio, R Franzosi, N Cristianini; Natural Language Engineering, 1-32, 2013
  14. ↑ Automated analysis of the US presidential elections using Big Data and network analysis; S Sudhahar, GA Veltri, N Cristianini; Big Data & Society 2 (1), 1-28, 2015
  15. ↑ Lansdall-Welfare TO, Sudhahar S, Veltri GA, Cristianini N. On the Coverage of Science in the Media: A Big Data Study on the Impact of the Fukushima Disaster. In Big Data (Big Data), 2014 IEEE International Conference on. IEEE, New York. 2014. p. 60-66.
  16. ↑ "When physicists do linguistics", BEN ZIMMER, Boston Globe, February 10, 2013

Further reading

External links

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