Skip to main content
SpringerLink
Log in

Social Big Data: Case Studies

  • Chapter
  • First Online:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 12630))

Abstract

Based on the concepts and theory that we have introduced in another paper “Social Big Data: Concepts and Theory” in this issue, we will concretely explain hypothesis generation and integrated analysis through use cases in this paper.

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

Access this chapter

Log in via an institution
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 16.99
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

  • Behavioral economics. https://www.behavioraleconomics.com/resources/mini-encyclopedia-o2019f-be/prospect-theory/ Accessed August 2019

  • Chen, X.-W., Lin, X.: BigData Deep Learning: Challenges and Perspectives 2014 IEEE Access 2

    Google Scholar 

  • Cookpad. https://cookpad.com/ Accessed 2019

  • Endo, M., Shoji, Y.S.S.S., Hirota, M., Ohno, S., Ishikawa, H.: Best-time estimation for regions and tourist spots using phenological observations with geotagged tweets. Int. J. Informat. Soc. (IJIS) 9(3), 109–117 (2017).

    Google Scholar 

  • Ester et al.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996).

    Google Scholar 

  • Fujii,S., Hirota, M., Kato, D., Araki, T., Endo, M., Ishikawa, H.: Analysis of the difference of movement trajectory by residents and tourists using geotagged tweet. In: Proceedings of the 11th International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing2019) (2019).

    Google Scholar 

  • Hara, A., Yamamoto, Y., Araki, T., Shibata, M., Ishikawa, H.: Identification of moon central peak craters by machine learning using Kaguya DEM. J. Space Sci. Informat. Japan, 8, pp. 1–10 (2019). (in Japanese). https://doi.org/10.20637/JAXA-RR-18-008/0001

  • Japanese Government, Tourism Vision to Support the Future of Japan. https://www.kantei.go.jp/jp/singi/kanko_vision/pdf/honbun.pdf. Accessed 2019

  • JAXA, KAGUY(SELENE) Data Archive. https://darts.isas.jaxa.jp/planet/pdap/selene/index.html. Accessed 2019.

  • Kanno, M., Ehara, Y., Hirota, M., Yokoyama, S., Ishikawa, H.: Visualizing high-risk paths using geo-tagged social data for disaster mitigation. In: Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-based Social Networks (2016)

    Google Scholar 

  • Kato, K., Yamada, R., Yamamoto, Y., Shibata, M., Yokoyama, S., Ishikawa, H.: Investigation of orbit parameters to classify the deep moonquake source. J. Space Sci. Informat. Japan 7, 43–52 (2018). (in Japanese). https://doi.org/10.20637/JAXA-RR-17-009/0005

  • Kikuchi, S., Yamada, R., Yamamoto, Y., Hirota, M., Yokoyama, S., Ishikawa, H.: Classification of unlabeled deep moonquakes using machine learning. In: Proceedings of the 9th International Conferences on Advances in Multimedia (MMEDIA 2017) (2017).

    Google Scholar 

  • LeCun, Y., Bengio, Y., Hinton, G.: Deep learning, vol. 521, pp. 436–444 (2015). https://doi.org/10.1038/nature14539

  • Lei, K., Zeng, Y.F.: A novel biased diversity ranking model for query-oriented multi-document summarization Appl. Mech. Mater. 380–384, 2811–2816 (2013). https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.835.1759&rep=rep1&type=pdf Accessed 2019

  • MeCab. https://taku910.github.io/mecab/. Accessed 2019

  • Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representation in vector space. arXiv: 1301.3781. Accessed 2019

    Google Scholar 

  • Ministry of Land, Infrastructure, Transport and Tourism, National Land Numerical Information Download Service, Evacuation Facility Data. https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-P20.html. Accessed 2019

  • Ministry of Land, Infrastructure, Transport and Tourism Meteorological Agency, Data on phenological observations. https://www.data.jma.go.jp/sakura/data/index.html. Accessed 2019

  • Mitomi, K., Endo, M., Hirota, M., Yokoyama, S., Shoji, Y., Ishikawa, H.: How to find accessible free Wi-Fi at tourist spots in Japan. In: Spiro, E., Ahn, Y.Y. (Eds.) Informatics Social. SocInfo 2016. Lecture Notes In Computer Science, vol. 10046. Springer, Cham (2016)

    Google Scholar 

  • Miura, R., Hirota, M., Kato, D., Araki, T., Endo, M., Ishikawa, H.: Predicting user gender on social media sites using geographical information. In: Proceedings of the ACM 10th International Conference on Management of Digital EcoSystems (MEDES2018) (2018)

    Google Scholar 

  • Miyata, Y., Ishikawa, H.: Accelerating analysis of frequently gathered data with continuous density based clustering. In: Proceedings of 10th ACM International Conference on Management of Digital EcoSystems (MEDES2018) (2018)

    Google Scholar 

  • Miyata, Y., Ishikawa, H.: Concept drift detection on data stream for revising DBSCAN cluster. In: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics (WIMS2020) (2020)

    Google Scholar 

  • Nobumoto, K., Hirota, M., Kato, D., Ishikawa, H.: Multilingualization of cooking by analogical description. J. Japan Soc. Fuzzy Theory Intell. Informat. 31(1), 526–553 (2019). (in Japanese)

    Google Scholar 

  • OECD, Evidence in Education: Linking Research and Policy. https://www.oecd.org/education/ceri/evidenceineducationlinkingresearchandpolicy.htm. Accessed 2019

  • Oliver, M.A., Kriging, W.R.: A method of interpolation for geographical information systems. Int. J. Geographic Inf. Syst. 4, 313–332 (1990)

    Google Scholar 

  • pgRouting. https://pgrouting.org/. Accessed 2019

  • Shinjuku Station Area Disaster Prevention Measures Council, Shinjuku Rule. https://www.city.shinjuku.lg.jp/content/000201844.pdf. Accessed 2019

  • Szakaly, A.: 7 Data science principles introduced by Asimov in the foundation (2017). https://Easymarketingmath.Com/2017/01/7-data-science-principles-asimov/. Accessed 2019

  • Bureau of Urban Development Tokyo Metropolitan Government, Emergency response difficulty. https://www.toshiseibi.metro.tokyo.jp/bosai/chousa_6/download/kikendo_06.pdf?1802. Accessed 2019

  • Tourist culture, tide management of destination management, No. 234 (July 2017). https://www.jtb.or.jp/wp-content/uploads/2017/07/bunka234.pdf. Accessed 2019

  • Tooshima, M., Kato, D., Endo, M., Shoji, Y., Hirota, M., Ishikawa, H.: Finding spots that disbelief based on differences in polarity of microblogging. In: Proceedings of the 9th Forum on data engineering and information management (DEIM) (2017). (in Japanese)

    Google Scholar 

  • Tsuchida, T.: On calculation of the meanings of the areas and landmarks using Word2Vec. In: Proceedings of the 8th Forum on Data Engineering and Information Management (DEIM) (2016). (in Japanese)

    Google Scholar 

  • Tsuchida, T., Kato, D., Endo, M., Hirota, M., Araki, T., Ishikawa, H.: Analyzing relationships of words using BiasedLexRank from Geotagging Tweets. In: Proceedings of ACM International Conference on Management of Digital EcoSystem (MEDES2017) (2017)

    Google Scholar 

  • Wikipedia, Arthur C. Clarke. https://en.wikipedia.org/wiki/Arthur_C._Clarke. Accessed 2019

  • Wikipedia, Isaac Asimov. https://en.wikipedia.org/wiki/Isaac_Asimov. Accessed 2019

  • Wikipedia, Jules_Verne. https://en.wikipedia.org/wiki/Jules_Verne. Accessed 2019

  • Zeng, D.: Crystal Balls, Statistics, Big Data, and Psychohistory: Predictive Analytics and Beyond. IEEE Intell. Syst. 30(2), 2–4 (2015). https://doi.org/10.1109/MIS.2015.24. Accessed2019

    Google Scholar 

Download references

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 20K12081, Tokyo Metropolitan University Grant-in-Aid for Research on Priority Areas, and Nomura School of Advanced Management Research Grant.

Author information

Authors and Affiliations

  1. Tokyo Metropolitan University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Hitachi, Ltd., Tokyo, Japan

    Yasushi Miyata

Authors
  1. Hiroshi Ishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasushi Miyata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroshi Ishikawa .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. IFS, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  3. University of Pau and the Adour Region, Anglet, France

    Richard Chbeir

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ishikawa, H., Miyata, Y. (2021). Social Big Data: Case Studies. In: Hameurlain, A., Tjoa, A.M., Chbeir, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVII. Lecture Notes in Computer Science(), vol 12630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62919-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-62919-2_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-62918-5

  • Online ISBN: 978-3-662-62919-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation