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Connecting Social Media Data with Observed Hybrid Data for Environment Monitoring

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Intelligent Distributed Computing XI (IDC 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 737))

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Abstract

Environmental monitoring has been regarded as one of effective solutions to protect our living places from potential risks. Traditional methods rely on periodically recording assessments of observed objects, which results in large amount of hybrid data sets. Additionally public opinions regarding certain topics can be extracted from social media and used as another source of descriptive data. In this work, we investigate how to connect and process the public opinions from social media with hybrid observation records. Particularly, we study Twitter posts from designated region with respect to specific topics, such as marine environmental activities. Sentiment analysis on tweets is performed to reflect public opinions on the environmental topics. Additionally two hybrid data sets have been considered. To process these data we use Hadoop cluster and utilize NoSql and relational databases to store data distributed across nodes in share nothing architecture. We compare the public sentiments in social media with scientific observations in real time and show that the “citizen science” enhanced with real time analytics can provide avenue to nominatively monitor natural environments. The approach presented in this paper provides an innovative method to monitor environment with the power of social media analysis and distributed computing.

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Notes

  1. 1.

    https://theconversation.com/can-big-data-studies-know-your-thoughts-and-predict-who-will-win-an-election-63110.

  2. 2.

    https://phys.org/news/2016-11-big-analyticsnostradamus-21st-century.html.

  3. 3.

    Apache Hadoop, http://hadoop.apache.org/.

  4. 4.

    https://www.brandwatch.com/blog/44-twitter-stats-2016/.

  5. 5.

    https://dev.twitter.com/streaming/overview.

  6. 6.

    http://www.mongodb.org/.

  7. 7.

    http://www.gbrmpa.gov.au/managing-the-reef/how-the-reefs-managed/eye-on-the-reef.

  8. 8.

    http://www.coralwatch.org/web/guest/home1.

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Acknowledgements

This project was funded through a National Environment Science Program (NESP) fund, within the Tropical Water Quality Hub (Project No: 2.3.2). We would also like to thank the Great Barrier Reef Marine Park Authority and CoralWatch for providing citizen science data for the purpose of this research.

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Authors and Affiliations

  1. Griffith Institute for Tourism, Brisbane, Australia

    Jinyan Chen

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

    Sen Wang & Bela Stantic

Authors
  1. Jinyan Chen
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  2. Sen Wang
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  3. Bela Stantic
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Corresponding author

Correspondence to Bela Stantic .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanović

  2. Faculty of Automatics, Computer Science and Electronics, University of Craiova, Craiova, Romania

    Costin Bădică

  3. Institut für Informatik, Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

    Jürgen Dix

  4. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Zoran Jovanović

  5. Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universita degli Studi di Catania, Catania, Italy

    Michele Malgeri

  6. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Miloš Savić

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Chen, J., Wang, S., Stantic, B. (2018). Connecting Social Media Data with Observed Hybrid Data for Environment Monitoring. In: Ivanović, M., Bădică, C., Dix, J., Jovanović, Z., Malgeri, M., Savić, M. (eds) Intelligent Distributed Computing XI. IDC 2017. Studies in Computational Intelligence, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-66379-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-66379-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66378-4

  • Online ISBN: 978-3-319-66379-1

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