Multi-hazard Detection by Integrating Social Media and Physical Sensors
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.
KeywordsLITMUS Social media Physical sensor Disaster management Landslide detection
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.
- 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