Skip to main content

Advertisement

SpringerLink
Log in

Use of large web-based data to identify public interest and trends related to endangered species

  • Original Paper
  • Published:
Biodiversity and Conservation Aims and scope Submit manuscript

Abstract

Use of the Internet by an increasing number of people to search for information related to varying disciplines has led to more precise data on societal views and trends. We used web search log data from a 6-year period and examined characteristics related to public interest in endangered species. Web search data for 246 endangered species as determined by the Ministry of Environment of Korea were evaluated. Relative search volumes for species were correlated with the status of conservation practices, and a self-organizing map (SOM) was used to analyze the relationship among selected variables. The relative search volume for higher taxa, including mammals, birds, amphibians and reptiles, were ten times higher than those for other taxa. SOM clusters were mainly divided according to the rank designation of the endangered species and the existence of a conservation facility dedicated to the species. The relative search volume and amount of web materials were the highest for the most highly ranked species. A positive relationship between the relative search volume and number of printed media articles (β 2.68; R 2 0.45; p < 0.0,001) and a negative relationship between the length of the common name of a species and number of printed media articles (β −125.7; R 2 0.48; p = 0.024) were found. The evaluation of endangered species by using web-based data can be useful to improve conservation tools, including using feedback to facilitate interaction among political, scientific, and socio-economic interests.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Achrekar H, Gandhe A, Lazarus R, Yu SH, Liu B (2011) Predicting flu trends using twitter data. In: Computer communications workshops (INFOCOM WKSHPS), 2011 IEEE conference. pp 702–707

  • Ahern J, Margerison-Zilko C, Hubbard A, Galea S (2013) Alcohol outlets and binge drinking in urban neighborhoods: the implications of nonlinearity for intervention and policy. Am J Public Health 103(4):81–87

    Article  Google Scholar 

  • Akamai (2012) State of the internet. http://www.akamai.com/stateoftheinternet/. Accessed 01 July 2013

  • AOL Inc. (2013) AOL search trends. http://search.aol.com/aol/trends. Accessed 01 April 2013

  • Baddeley A (1992) Working memory. Science 255:556–559

    Article  PubMed  CAS  Google Scholar 

  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8

    Article  Google Scholar 

  • Bowen-Jones E, Entwistle A (2002) Identifying appropriate flagship species: the importance of culture and local contexts. Oryx 36:189–195

    Article  Google Scholar 

  • Carneiro HA, Mylonakis E (2009) Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin Infect Dis 49:1557–1564

    Article  PubMed  Google Scholar 

  • Caro T (2010) Conservation by proxy: indicator, umbrella, keystone, flagship, and other surrogate species. Island Press, Washington, D.C.

    Google Scholar 

  • Céréghino R, Park YS (2009) Review of the self-organizing map (SOM) approach in water resources: commentary. Environ Model Softw 24:945–947

    Article  Google Scholar 

  • Chakrabarty S, Bhattacharya S, Batra K (2013) On evaluation of customer dissatisfaction index based on sentiment analysis: a statistical approach. Int J Data Anal Tech Strat 5(4):427–444

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chon TS (2011) Self-organizing maps applied to ecological sciences. Ecol Inform 6(1):50–61

    Article  Google Scholar 

  • Chon TS, Qu X, Cho WS, Hwang HJ, Tang H, Liu Y, Choi JH (2013) Evaluation of stream ecosystem health and species association based on multi-taxa (benthic macroinvertebrates, algae, and microorganisms) patterning with different levels of pollution. Ecol Inform 17:58–72

    Article  Google Scholar 

  • Clements CF (2013) Public interest in the extinction of a species may lead to an increase in donations to a large conservation charity. Biodivers Conserv 22:2695–2699

    Article  Google Scholar 

  • Clucas B, McHugh K, Caro T (2008) Flagship species on covers of US conservation and nature magazines. Biodivers Conserv 17:1517–1528

    Article  Google Scholar 

  • Daum communications (2013) Daum. http://trend.search.daum.net/SearchTrend/index.html. Accessed 01 July 2013

  • Dawson D, Shogren JF (2001) An update on priorities and expenditures under the Endangered Species Act. Land Econ 77:527–532

    Article  Google Scholar 

  • Fazey I, Fischer J, Lindenmayer DB (2005) What do conservation biologists publish? Biol Conserv 124:63–73

    Article  Google Scholar 

  • Ficetola GF (2013) Is interest toward the environment really declining? The complexity of analysing trends using internet search data. Biodivers Conserv 22:2983–2988

    Article  Google Scholar 

  • Fischer A, Bednar-Friedl B, Langers F, Dobrovodská M, Geamana N, Skogen K, Dumortier M (2011) Universal criteria for species conservation priorities? Findings from a survey of public views across Europe. Biol Conserv 144:998–1007

    Article  Google Scholar 

  • Goel S, Hofman JM, Lahaie S, Pennock DM, Watts DJ (2010) Predicting consumer behavior with Web search. Proc Natl Acad Sci 107:17486–17490

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Google (2013) Google Trends. http://www.google.com/trends/. Accessed 01 July 2013

  • International Telecommunication Union (2013) Worldwide internet users. http://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx. Accessed 10 April 2013

  • Internet TrendTM (2013) Web search statistics. http://internettrend.co.kr/trendForward.tsp. Accessed 01 July 2013

  • Israel N, Wolf-Branigin M (2011) Nonlinearity in social service evaluation: a primer on agent-based modeling. Soc Work Res 35(1):20–24

    Article  Google Scholar 

  • IUCN (2013) The IUCN Red List of Threatened Species. http://www.iucnredlist.org. Accessed 01 July 2013

  • Jansen BJ, Liu Z, Weaver C, Campbell G, Gregg M (2011) Real time search on the web: queries, topics, and economic value. Inform Process Manag 47(4):491–506

    Article  Google Scholar 

  • Kohonen T (2001) Self-organizing maps. Springer Science & Business Media, Berlin, Germany

  • Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65

    Article  PubMed  Google Scholar 

  • Lindemann-Matthies P, Bose E (2008) How many species are there? Public understanding and awareness of biodiversity in Switzerland. Hum Ecol 36:731–742

    Article  Google Scholar 

  • Male TD, Bean MJ (2005) Measuring progress in US endangered species conservation. Ecol Lett 8:986–992

    Article  Google Scholar 

  • Martín-López B, Montes C, Benayas J (2008) Economic valuation of biodiversity conservation: the meaning of numbers. Conserv Biol 22:624–635

    Article  PubMed  Google Scholar 

  • Martín-López B, Montes C, Ramírez L, Benayas J (2009) What drives policy decision-making related to species conservation? Biol Conserv 142:1370–1380

    Article  Google Scholar 

  • Mccallum ML, Bury GW (2013) Google search patterns suggest declining interest in the environment. Biodivers Conserv 22:1355–1367

    Article  Google Scholar 

  • McKinney ML (1999) High rates of extinction and threat in poorly studied taxa. Conserv Biol 13:1273–1281

    Article  Google Scholar 

  • Microsoft (2013) Bing Trends. http://www.bing.com/toolbox/keywords. Accessed 01 April 2013

  • Ministry of Environment (2012) List of endangered species. Ministry of Environment, Seoul

    Google Scholar 

  • Musiał K, Kazienko P (2013) Social networks on the internet. World Wide Web 16(1):31–72

    Article  Google Scholar 

  • NHN (2013) Naver Trends. http://trend.naver.com/. Accessed 01 July 2013

  • Novacek MJ (2008) Engaging the public in biodiversity issues. Proc Natl Acad Sci 105(1):11571–11578

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • OECD (2010) OECD Broadband statistics. http://oecd.org/sti/ict/broadband. Accessed 01 July 2013

  • Park YS, Chang J, Lek S, Cao W, Brosse S (2003) Conservation strategies for endemic fish species threatened by the Three Gorges Dam. Conserv Biol 17:1748–1758

    Article  Google Scholar 

  • Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron AJ (2009) More diseases tracked by using Google Trends. Emerg Infect Dis 15:1327

    Article  PubMed  PubMed Central  Google Scholar 

  • Ripberger JT (2011) Capturing curiosity: using internet search trends to measure public attentiveness. Policy Stud J 39(2):239–259

    Article  Google Scholar 

  • Rzhetsky A, Seringhaus M, Gerstein M (2008) Seeking a new biology through text mining. Cell 134(1):9–13

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Seifter A, Schwarzwalder A, Geis K, Aucott J (2010) The utility of “Google Trends” for epidemiological research: lyme disease as an example. Geosp Health 4:135–137

    Google Scholar 

  • Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Champaign

    Google Scholar 

  • Sparrow B, Liu J, Wegner DM (2011) Google effects on memory: cognitive consequences of having information at our fingertips. Science 333(6043):776–778

    Article  PubMed  CAS  Google Scholar 

  • Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11:586–600

    Article  PubMed  CAS  Google Scholar 

  • Wilson JRU, Procheş Ş, Braschler B, Dixon ES, Richardson DM (2007) The (bio)diversity of science reflects the interests of society. Front Ecol Environ 5:409–414

    Article  Google Scholar 

  • Wilson N, Mason K, Tobias M, Peacey M, Huang QS, Baker M (2009) Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. Eur Surveill 14:19386

    Google Scholar 

  • YouTube (2013) YouTube Trends. http://youtube-trends.blogspot.kr/. Accessed 01 April 2013

  • Żmihorski M, Dziarska-Pałac J, Sparks TH, Tryjanowski P (2013) Ecological correlates of the popularity of birds and butterflies in Internet information resources. Oikos 122:183–190

    Article  Google Scholar 

Download references

Acknowledgments

This study was supported by the Korea National Long-Term Ecological Research (KNLTER) by the National Institute of Ecology, Ministry of Environment (S. Korea).

Author information

Authors and Affiliations

  1. Department of Integrated Biological Science, Pusan National University, Busan, 609-735, South Korea

    Ji Yoon Kim, Ran-Young Im, Gu-Yeon Kim & Gea-Jae Joo

  2. Department of Forest Protection and Ecology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776, Warszawa, Poland

    Yuno Do

Authors
  1. Ji Yoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuno Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ran-Young Im
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gu-Yeon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gea-Jae Joo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gea-Jae Joo.

Additional information

Communicated by Marcelo F Tognelli.

Appendix

Appendix

See Table 3.

Table 3 List of endangered species with their relative search volume, related  web materials, and printed media articles
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, J.Y., Do, Y., Im, RY. et al. Use of large web-based data to identify public interest and trends related to endangered species. Biodivers Conserv 23, 2961–2984 (2014). https://doi.org/10.1007/s10531-014-0757-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10531-014-0757-8

Keywords

Search

Navigation