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).
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References
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)
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
Baker, S.: What drives job search. Evidence from Google search data. [Electronic resource], Technical report, Stanford University (2011)
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)
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)
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)
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)
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)
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)
Choi, H.: Predicting the present with google trends, [Electronic resource] (2011). http://people.ischool.berkeley.edu/~hal/Papers/2011/ptp.pdf
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/
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)
Danilova, V.: Linguistic support for protest event data collection. Ph.D. thesis, Autonomous University of Barcelona (2015)
Ginsberg, J., et al.: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)
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
GMDH Shell: Algorithms of inductive modeling www.gmdhshell.com/
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
Google Resource: Google trends. www.google.ru/trends
Google Resource: AdWords. www.google.com/adwords/
Google Resource: Search patterns. www.google.com/trends/correlate/
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
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
Lyashevskaya, O.: Frequent vocabulary of modern Russian. Publication House Azbukovnik [rus] (2009). dict.ruslang.ru/freq.php
Madala, H., Ivakhnenko, A.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton (1994)
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)
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
Program platform GMDH Shell. www.gmdhshell.com
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
Russian Resource: Economical vocabulary of terms. www.economicportal.ru/terms.html
Russian Resource: Large juridical vocabulary (terms, notions). www.petrograd.biz/dictionaries/dict_big_law.php
Russian Resource: General office of Russian public prosecutor, legal statistics. crimestat.ru/offenses_map
Russian Resource: Data base of exchange terms. www.multitran.ru/c/m.exe?a=110&s=%E0&sc=67&dict=
Terms (English): Money in motion, key terms vocabulary. www.cnbc.com/id/100001502
Terms (English): Economics A-Z terms. www.economist.com/economics-a-to-z/a
Schmidt, T.: Forecasting private consumption: survey-based indicators vs. Google trends, [Electronic resource] (2009). http://ideas.repec.org/p/rwi/repape/0155.html
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)
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.
Yandex statistics. wordstat.yandex.com
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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
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DOI: https://doi.org/10.1007/978-3-319-62434-1_26
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