Skip to main content

Creating Collections of Descriptors of Events and Processes Based on Internet Queries

  • Conference paper
  • First Online:
Advances in Computational Intelligence (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10061))

Included in the following conference series:

Abstract

Search queries to Internet are a real reflection of events and processes that happen in the informative society. Moreover, the recent research shows that search queries can be an effective tool for the analysis and forecast of these events and processes. In the paper, we present our experience in creating databases of descriptors (queries and their combinations) to be used in real problems. An example related to the analysis and forecast of regional economy illustrates an application of the mentioned descriptors. The paper is intended for those who use or plan to use Internet queries in their applied research and practical applications.

Work done under partial support of the Institute of Applied Economic Research under Russian Presidential Academy of national economy and public administration (RANEPA).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alexandrov, M., Danilova, V., Koshulko, A, Tejada, J.: Models for opinion classication of blogs taken from Peruvian Facebook. In: Proceedings of 4th International Conference on Inductive Modeling (ICIM-2013), pp. 241–246. Publication House ITRC-NASU, Kyev (2013)

    Google Scholar 

  2. Baccinella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resources for sentiment analysis and opinion mining. In: LREC10, pp. 2200–2204 (2010). http://nmis.isti.cnr.it/sebastiani/Publications/LREC10.pdf

  3. Baker, S.: What drives job search. Evidence from Google search data. [Electronic resource], Technical report, Stanford University (2011)

    Google Scholar 

  4. Boldyreva, A., Alexandrov, M., Surkova, D.: Negative words in search queries to internet as an indicator of average per capita incomes in Federal regions of Russia. In: Inductive Modeling of Complex Systems, NASU (Ukraine), vol. 7, pp. 77–92 [rus] (2015)

    Google Scholar 

  5. Boldyreva, A.: Demographic forecasts based on queries to Yandex search machine. In: Proceeding of International Workshop on Inductive Modeling, pp. 7–8. Publication House ITRC-NASU (Ukraine) and Czech Technical University, Kyev (2015)

    Google Scholar 

  6. Boldyreva, A., Koshulko, O.: Forecasting models of economic crimes based on queries to internet: Regression vs. GMDH. In: Proceeding of Sociological Faculty on Mathematical Modeling of Social Processes, vol. 17, pp. 34–42. Lomonosov Moscow State University [rus] (2015)

    Google Scholar 

  7. Boldyreva, A., Koshulko, O.: GMDH helps to build models based on queries to Yandex for forecast of economic crimes. In: Proceeding of International Workshop on Inductive Modeling, pp. 9–11. Publication House ITRC-NASU (Ukraine) and Czech Technical University, Kyev (2015)

    Google Scholar 

  8. Boldyreva, A.: Method for assessing moods of Internet users with search queries (pilot study of Russian regions). In: Proceeding of Sociological Faculty on Mathematical Modeling of Social Processes, vol. 18, pp. 26–34. Lomonosov Moscow State University [rus] (2016)

    Google Scholar 

  9. Boldyreva, A.: Building models for analysis and forecast of economic and social conjuncture using intensity of search queries to Internet. In: Modern Economy: Theory, Politics, Innovation, vol. 21, pp. 36–61, RANEPA, Moscow [rus] (2016)

    Google Scholar 

  10. Choi, H.: Predicting the present with google trends, [Electronic resource] (2011). http://people.ischool.berkeley.edu/~hal/Papers/2011/ptp.pdf

  11. Cooper, C.: Cancer internet search activity on a major search engine [Electronic resource]. J. Med. Internet Res. 7, e36 (2005). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1550657/

  12. Danilova, V.: A pipeline for multilingual protest event selection and annotation. In: Proceeding of TIR-2015 Workshop (Text-based Information Retrieval), pp. 309–314. IEEE (2015)

    Google Scholar 

  13. Danilova, V.: Linguistic support for protest event data collection. Ph.D. thesis, Autonomous University of Barcelona (2015)

    Google Scholar 

  14. Ginsberg, J., et al.: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)

    Article  Google Scholar 

  15. Huang, H.: Constructing consumer sentiment index for U.S using Google searches. [Electronic resource], Technical report, University of Alberta (2009) http://econpapers.repec.org/paper/risalbaec/2009_5f026.htm

  16. GMDH Shell: Algorithms of inductive modeling www.gmdhshell.com/

  17. Goel, S., et al.: Predicting consumer behavior with Web search Proc. USA Acad. Sci. 107(41), 17486–17490 (2010). www.pnas.org/content/107/41/17486.full

  18. Google Resource: Google trends. www.google.ru/trends

  19. Google Resource: AdWords. www.google.com/adwords/

  20. Google Resource: Search patterns. www.google.com/trends/correlate/

  21. Google Resource: Protests in France. www.google.ru/trends/explore#q=marche%2C%20rassemblement%2C%20police%2C%20lutte&geo=FR&cmpt=q&tz=Etc%2FGMT-6

  22. Kang, M., et al.: Using google trends for influenza surveillance in South China. PLoS ONE 8(1), e55205 (2013). doi:10.1371/journal.pone.005520

    Article  Google Scholar 

  23. Lyashevskaya, O.: Frequent vocabulary of modern Russian. Publication House Azbukovnik [rus] (2009). dict.ruslang.ru/freq.php

  24. Madala, H., Ivakhnenko, A.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton (1994)

    MATH  Google Scholar 

  25. Preis, T.: Complex dynamics of our economic life on different scales: insights from search engine query data [Electronic Resource]. Phil. Trans. R. Soc. A 368, 5707–5719 (2010)

    Article  MATH  Google Scholar 

  26. Preis, T.: Quantifying the semantics of search behavior before stock market moves Proc. Nat. Acad. Sci. USA 111, 11600–11605 (2013). http://www.ncbi.nlm.nih.gov/pubmed/23619126

  27. Program platform GMDH Shell. www.gmdhshell.com

  28. Radinsky, K.: Predicting the news of tomorrow using patterns in web search queries [Electronic resource]. In: Proceeding of IEEE/WIC/ACM International Conference on Web Intelligence (WI2008) (2009). http://portal.acm.org/citation.cfm?id=1487070

  29. Russian Resource: Economical vocabulary of terms. www.economicportal.ru/terms.html

  30. Russian Resource: Large juridical vocabulary (terms, notions). www.petrograd.biz/dictionaries/dict_big_law.php

  31. Russian Resource: General office of Russian public prosecutor, legal statistics. crimestat.ru/offenses_map

  32. Russian Resource: Data base of exchange terms. www.multitran.ru/c/m.exe?a=110&s=%E0&sc=67&dict=

  33. Terms (English): Money in motion, key terms vocabulary. www.cnbc.com/id/100001502

  34. Terms (English): Economics A-Z terms. www.economist.com/economics-a-to-z/a

  35. Schmidt, T.: Forecasting private consumption: survey-based indicators vs. Google trends, [Electronic resource] (2009). http://ideas.repec.org/p/rwi/repape/0155.html

  36. Stepashko, V.: Ideas of academician O. Ivakhnenko in Inductive Modeling field from historical perspective. In: Proceeding of 4th International Conference on Inductive Modeling (ICIM-2013), pp. 31–37. Publication House NAS of Ukraine, Prague Technical University, Kyev (2013)

    Google Scholar 

  37. Stepashko, V.: Method of critical variances as an analytical tool of the Inductive Modeling Theory. J. Inform. Automat. Sci. 40(3), 47–58 (2008). Begell House Inc.

    Article  Google Scholar 

  38. Yandex statistics. wordstat.yandex.com

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna Boldyreva .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Boldyreva, A., Sobolevskiy, O., Alexandrov, M., Danilova, V. (2017). Creating Collections of Descriptors of Events and Processes Based on Internet Queries. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62434-1_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62433-4

  • Online ISBN: 978-3-319-62434-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics