Skip to main content

Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends

  • Chapter
  • First Online:
Decisions and Trends in Social Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 189))

Abstract

Internet search engines have become a popular and readily accessible source of information. Google Trends, by means of analyzing the popularity of search queries in Google Search, allows to provide deep insights into population behavior. Interestingly, Google is increasingly being used also to obtain health-related information, as well as to self-prescribe one’s dietary intake. In particular, we analysed the search traffic related to the keywords Mediterranean diet since it has always been very popular. More specifically, we propose to use Google Trends data as proxies for the interest in Mediterranean diet and to analyze them through the functional data analysis (FDA) approach.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. Aguilera AM, Fortuna F, Escabias M, Di Battista T (2019) Assessing social interest in burnout using Google trends data. Ind Res Soc. https://doi.org/10.1007/s11205-019-02250-5

    Article  Google Scholar 

  2. Bach-Faig A, Berry E, Lairon D, Reguant J, Trichopoulou A, Dernini S, Medina F, Battino M, Belahsen R, Miranda G, Serra-Majem L (2011) Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr 14(12A):2274–2284. https://doi.org/10.1017/S1368980011002515

  3. Caruso G, Di Battista T, Gattone SA (2019) A micro-level analysis of regional economic activity through a PCA approach. In: Bucciarelli E, Chen S, Corchado JM (eds) Decisions economics: complexity of decisions and decisions for complexity. Advances in intelligent systems and computing (in print)

    Google Scholar 

  4. Caruso G, Gattone SA (2019) Waste management analysis in developing countries through unsupervised classification of mixed data. Soc Sci 8(6)

    Google Scholar 

  5. Caruso G, Gattone SA, Balzanella A, Di Battista T (2019) Cluster analysis: an application to a real mixed-type data set. In: Flaut C, Hoskova-Mayerova S, Flaut D (eds) Models and theories in social systems. Studies in systems, decision and control, vol 179. Springer, pp 525–533

    Google Scholar 

  6. Caruso G, Gattone SA, Fortuna F, Di Battista T (2018) Cluster analysis as a decision-making tool: a methodological review. In: Bucciarelli E, Chen S, Corchado, JM (eds) Decision economics: in the tradition of Herbert A. Simon’s heritage. Advances in intelligent systems and computing, vol 618. Springer, pp 48–55

    Google Scholar 

  7. Choi HY, Varian H (2012) Predicting the present with Google Trends. Econ Rec 88:2–9

    Article  Google Scholar 

  8. D’Adamo I, Falcone PM, Gastaldi M (2019) Price analysis of extra virgin olive oil. Brit Food J. https://doi.org/10.1108/BFJ-03-2019-0186

  9. De Boor C (2001) A practical guide to splines. Springer, New York

    MATH  Google Scholar 

  10. Demarin V, Lisak M, Morović S (2011) Mediterranean diet in healthy lifestyle and prevention of stroke. Acta Clin Croat 50(1):67–76

    Google Scholar 

  11. Di Battista T, Fortuna F (2016) Clustering dichotomously scored items through functional k-means algorithm. EJASA 9(2):433–450

    Google Scholar 

  12. Di Battista T, Fortuna F (2017) Functional confidence bands for lichen biodiversity profiles: a case study in Tuscany region (central Italy). Stat Anal Data Min 10(1):21–28

    Article  MathSciNet  Google Scholar 

  13. Di Battista T, Gattone SA, De Sanctis A (2010) Dealing with FDA estimation methods. In: Ingrassia S, Rocci R, Vichi M (eds) New perspectives in statistical modeling in data analysis. Advances in intelligent systems and computing. Springer, Cham, pp 357–365

    Google Scholar 

  14. Ferreira L, Hitchcock D (2009) A comparison of hierarchical methods for clustering functional data. Commun Stat-Simul C 38(9):1925–1949

    Article  MathSciNet  Google Scholar 

  15. Forgy E (1965) Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics 21:768–769

    Google Scholar 

  16. Fortuna F, Maturo F, Di Battista T (2018) Clustering functional data streams: unsupervised classification of soccer top players based on Google trends. Qual Reliab Eng Int 34(7):1448–1460

    Article  Google Scholar 

  17. Golley S, Corsini N, Mohr P (2017) Managing symptoms and health through self-prescribed restrictive diets: what can general practitioners learn from the phenomenon of wheat avoidance? Aust Fam Phys 46:603–608

    Google Scholar 

  18. Jain A, Dubes R (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs, NY

    MATH  Google Scholar 

  19. King R (1971) The “Questione Meridionale” in Southern Italy, vol 11. University of Durham, Department of Geography

    Google Scholar 

  20. Klement R, Frobel T, Albers T, Fikenzer S, Prinzhausen J, Kämmerer U (2013) A pilot case study on the impact of a self-prescribed ketogenic diet on biochemical parameters and running performance in healthy and physically active individuals. Nutr Med 1

    Google Scholar 

  21. Lambert SD, Loiselle CG (2007) Health information-seeking behavior. Qual Health Res 17(8):1006–1019. https://doi.org/10.1177/1049732307305199

    Article  Google Scholar 

  22. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, 1967. University of California Press

    Google Scholar 

  23. Maturo F, Hoskova-Mayerova S (2016) Fuzzy regression models and alternative operations for economic and social sciences. In: Recent trends in social systems: quantitative theories and quantitative models. Springer, Switzerland, pp 235–247. https://doi.org/10.1007/978-3-319-40585-8_21

  24. Maturo F, Hoskova-Mayerova S (2018) Analyzing research impact via functional data analysis: a powerful tool for scholars, insiders, and research organizations. In: Proceedings of the 31st international business information management association conference innovation management and education excellence through vision 2020, pp 1832–1842. ISBN 978-0-9998551-0-2

    Google Scholar 

  25. McQuitty L (1966) Similarity analysis by reciprocal pairs for discrete and continuous data. Educ Psychol Meas 27:21–46

    Article  Google Scholar 

  26. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI et al (2014) The use of Google Trends in health care research: a systematic review. PLoS ONE 9(10):e109583. https://doi.org/10.1371/journal.pone.0109583

    Article  Google Scholar 

  27. Pandey A, Hasan S, Dubey D, Sarangi S (2013) Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior. J Cancer Educ 28(1):138–142

    Article  Google Scholar 

  28. Ramsay JO, Silverman BW (2005) Functional data analysis, 2nd edn. Springer

    Google Scholar 

  29. Sangalli L, Secchi P, Vantini S, Vitelli V (2010) Functional clustering and alignment methods with applications. CAIM 1:205–224

    MathSciNet  MATH  Google Scholar 

  30. Sangalli L, Secchi P, Vantini S, Vitelli V (2010) K-mean alignment for curve clustering. Comput Stat Data Anal 54:1219–1233

    Article  MathSciNet  Google Scholar 

  31. Sneath P (1957) The application of computers to taxonomy. J Gen Microbiol 17:201–226

    Article  Google Scholar 

  32. Sokal R, Michener C (1958) A statistical method for evaluating systematic relationships. Univ Kansas Sci Bull 38:1409–1438

    Google Scholar 

  33. Tan K, Witten D (2015) Statistical properties of convex clustering. Electron J Stat 9:2324–2347

    Article  MathSciNet  Google Scholar 

  34. Tarpey T (2007) Linear transformations and the k-means clustering algorithm: applications to clustering curves. J Am Stat Assoc 61(1):34–40

    Article  MathSciNet  Google Scholar 

  35. Vance K, Howe W, Dellavalle RP (2009) Social internet sites as a source of public health information. Dermatol Clin 27(2):133–136

    Article  Google Scholar 

  36. Ward J (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

    Article  MathSciNet  Google Scholar 

  37. Willett WC, Sacks F, Trichopoulou A, Drescher G, Ferro-Luzzi A, Helsing E, Trichopoulos D (1995) Mediterranean diet pyramid: a cultural model for healthy eating. Am J Clin Nutr 61(6):1402–1406

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Caruso .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Caruso, G., Fortuna, F. (2021). Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends. In: Soitu, D., Hošková-Mayerová, Š., Maturo, F. (eds) Decisions and Trends in Social Systems. Lecture Notes in Networks and Systems, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-030-69094-6_6

Download citation

Publish with us

Policies and ethics