Skip to main content
SpringerLink
Log in

Multi-hazard Detection by Integrating Social Media and Physical Sensors

  • Chapter
  • First Online:
Social Media for Government Services

Abstract

Disaster Management is one of the most important functions of the government. FEMA and CDC are two examples of government agencies directly charged with handling disasters, whereas USGS is a scientific agency oriented towards disaster research. But regardless of the type or purpose, each of the mentioned agencies utilizes Social Media as part of its activities. One of the uses of Social Media is in detection of disasters, such as earthquakes. But disasters may lead to other kinds of disasters, forming multi-hazards such as landslides. Effective detection and management of multi-hazards cannot rely only on one information source. In this chapter, we describe and evaluate a prototype implementation of a landslide detection system LITMUS, which combines multiple physical sensors and Social Media to handle the inherent varied origins and composition of multi-hazards. Our results demonstrate that LITMUS detects more landslides than the ones reported by an authoritative source.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://www.cdc.gov/socialmedia/tools/guidelines/socialmediatoolkit.html.

  2. 2.

    https://twitter.com/usgsnewshazards.

  3. 3.

    http://www.geonames.org/.

  4. 4.

    https://developers.google.com/maps/documentation/geocoding/.

  5. 5.

    http://landslides.usgs.gov/recent/index.php?year=2014&month=Nov.

  6. 6.

    http://timesofindia.indiatimes.com/city/dehradun/Now-a-grass-that-could-prevent-landslides/articleshow/45196678.cms.

  7. 7.

    http://www.radionz.co.nz/news/regional/258610/pass-reopens-with-rock-fall-protection.

  8. 8.

    http://en.wikipedia.org/wiki/File:Minor_rockfall_on_Angeles_Crest_Highway_2014-11-05.jpg.

  9. 9.

    http://thecostaricanews.com/route-27-remains-closed-due-to-landslides.

  10. 10.

    https://grait-dm.gatech.edu/demo-multi-source-integration/.

References

  1. Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes twitter users: Real-time event detection by social sensors. In 19th International Conference on World Wide Web (WWW). Raleigh, North Carolina.

    Google Scholar 

  2. Wang, D., Irani, D., & Pu, C. (2011). A social-spam detection framework. In 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference. Perth, Australia.

    Google Scholar 

  3. Wang, D., Irani, D., & Pu, C. (2013). A study on evolution of email spam over fifteen years. In 9th International Conference Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom). Austin, Texas.

    Google Scholar 

  4. Wang, D. (2014). Analysis and detection of low quality information in social networks. In IEEE 30th International Conference on Data Engineering Workshops (ICDEW). Chicago, Illinois.

    Google Scholar 

  5. Musaev, A., Wang, D., & Pu, C. (2014). LITMUS: Landslide detection by integrating multiple sources. In 11th International Conference Information Systems for Crisis Response and Management (ISCRAM). Pennsylvania: University Park.

    Google Scholar 

  6. Musaev, A., Wang, D., Cho, C.-A., & Pu, C. (2014). Landslide detection service based on composition of physical and social information services. In 21st IEEE International Conference on Web Services (ICWS). Anchorage, Alaska.

    Google Scholar 

  7. Musaev, A., Wang, D., & Pu, C. (2014). LITMUS: A multi-service composition system for landslide detection. In IEEE Transactions on Services Computing (No. 99).

    Google Scholar 

  8. Ran, S. (2003). A model for web services discovery with QoS. In ACM SIGecom Exchanges (Vol. 4, no. 1).

    Google Scholar 

  9. Gangadharan, G., Weiss, M., DAndrea, V., & Iannella, R. (2007). Service license composition and compatibility analysis. In 5th International Conference on Service Oriented Computing (ICSOC). Vienna, Austria.

    Google Scholar 

  10. Redis: An open-source advanced key-value store. http://redis.io. Accessed January 1, 2015.

  11. Tropical Rainfall Measuring Mission (TRMM). http://trmm.gsfc.nasa.gov. Accessed January 1, 2015.

  12. Earthquakes Hazards Program, United States Geological Survey. http://earthquake.usgs.gov. Accessed January 1, 2015.

  13. Center for Hazards and Risk Research—CHRR—Columbia University, Center for International Earth Science Information Network—CIESIN—Columbia University, and Norwegian Geotechnical Institute—NGI. (2005). Global Landslide Hazard Distribution. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4P848VZ. Accessed January 1, 2015.

  14. Guy, M., Earle, P., Ostrum, C., Gruchalla, K., & Horvath, S. (2010). Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. In Intelligent Data Analysis IX. Tucson, Arizona.

    Google Scholar 

  15. Cameron, M. A., Power, R., Robinson, B., & Yin, J. (2012). Emergency situation awareness from twitter for crisis management. In 1st Workshop on Social Web for Disaster Management (SWDM). Lyon, France.

    Google Scholar 

  16. Gabrilovich, E., & Markovitch, S. (2006). Overcoming the brittleness bottleneck using Wikipedia: Enhancing text categorization with encyclopedic knowledge. In National Conference on Artificial Intelligence (AAAI). Boston, Massachusetts.

    Google Scholar 

  17. Gabrilovich, E., & Markovich, S. (2007). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In 20th International Joint Conference on Artificial Intelligence (IJCAI). Hyderabad, India.

    Google Scholar 

  18. Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: A content-based approach to geo-locating twitter users. In 19th ACM international conference on Information and Knowledge Management (CIKM). Toronto, Ontario, Canada.

    Google Scholar 

  19. Hecht, B., Hong, L., Suh, B., & Chi, E. H. (2011). Tweets from Justin Bieber’s heart: The dynamics of the “location” field in user profiles. In Conference on Human Factors in Computing Systems (CHI). Vancouver, Canada.

    Google Scholar 

  20. Sultanik, E. A., & Fink, C. (2012). Rapid geotagging and disambiguation of social media text via an indexed gazetteer. In 9th International Conference Information Systems for Crisis Response and Management (ISCRAM). Vancouver, Canada.

    Google Scholar 

Download references

Acknowledgements

This research has been partially funded by National Science Foundation by CNS/SAVI (1250260, 1402266), IUCRC/FRP (1127904), CISE/CNS (1138666, 1421561), NetSE (0905493) programs, and gifts, grants, or contracts from Fujitsu, Singapore Government, and Georgia Tech Foundation through the John P. Imlay, Jr. Chair endowment. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding agencies and companies mentioned above.

Author information

Authors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332, Atlanta, Georgia

    Aibek Musaev, De Wang & Calton Pu

Authors
  1. Aibek Musaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. De Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Calton Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aibek Musaev .

Editor information

Editors and Affiliations

  1. CSIRO Data61, Marsfield, New South Wales, Australia

    Surya Nepal

  2. Services Flagship, CSIRO Data61, Sydney, New South Wales, Australia

    Cécile Paris

  3. RMIT University, Melbourne, Victoria, Australia

    Dimitrios Georgakopoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Musaev, A., Wang, D., Pu, C. (2015). Multi-hazard Detection by Integrating Social Media and Physical Sensors. In: Nepal, S., Paris, C., Georgakopoulos, D. (eds) Social Media for Government Services. Springer, Cham. https://doi.org/10.1007/978-3-319-27237-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27237-5_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27235-1

  • Online ISBN: 978-3-319-27237-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation