Dhekar Abhik
ABSTRACT
Social networking sites such as Flickr, YouTube, Facebook, etc. contain a huge amount of user-contributed data for a variety of real-world events. These events can be some natural calamities such as earthquakes, floods, forest fires, etc. or some man-made hazards like riots. This work focuses on getting better knowledge about a natural hazard event using the data available from social networking sites. Rescue and relief activities in emergency situations can be enhanced by identifying sub-events of a particular event. Traditional topic discovery techniques used for event identification in news data cannot be used for social media data because social network data may be unstructured. To address this problem the features or metadata associated with social media data can be exploited. These features can be user-provided annotations (e.g., title, description) and automatically generated information (e.g., content creation time). Considerable improvement in performance is observed by using multiple features of social media data for sub-event detection rather than using individual feature. Proposed here is a two-step process. In the first step, clusters are formed from social network data using relevant features individually. Based on the significance of features weights are assigned to them. And in the second step all the clustering solutions formed in the first step are combined in a principal weighted manner to give the final clustering solution. Each cluster represents a sub-event for a particular natural hazard.
- J. Allan. Topic Detection and Tracking - Event-based Information Organization. Kluwer Academic Publisher, 2002. Google Scholar
Digital Library
- H. Becker, M. Naaman, and L. Gravano. Event identification in social media. Twelfth International Workshop on the Web and Databases, pp. 107--111, Providence, Rhode Island, USA, June 2009.Google Scholar
- Finding distances based on Latitude and Longitude using Haversine Formula. http://andrew.hedges.name/experiments/haversine/Google Scholar
- C. Hagar, and C. Haythornthwaite. Crisis, Farming & Community. The Journal of Community Informatics, Volume 1, Issue 3, 41--52, 2005.Google Scholar
- P. T. Jaeger, L. A. Langa, C. R. McClure, and J. C. Bertot. The 2004 and 2005 Gulf Coast Hurricanes: Evolving Roles and Lessons Learned for Public Libraries in Disaster Preparedness and Community Services. Public Library Quarterly, 25, 3/4, 199--214, 2007.Google Scholar
- P. T. Jaeger, K. R. Fleischmann, J. Preece, B. Shneiderman, and, P. F. Wu. Community response grids: using information technology to help communities respond to bioterror emergencies. Boisecurity and Bioterrorism: Biodefense Stratergy, Volume 5 (4), 335--346, December 2007.Google ScholarCross Ref
- G. Kumaram, and J. Allan. Text classification and named entities for new event detection. Proceedings of the 27th ACM International Conference on Research and Development in Information Retrieval (SIGIR), 297--304, 2004. Google ScholarDigital Library
- D. Pohl, A. Bouchachia, and H. Hellwagner. Automatic Sub-Event Detection in Emergency Management Using Social Media. Proceedings of the 21st international conference companion on World Wide Web, 683--686, Lyon, France, April 2012. Google ScholarDigital Library
- Y. Qu, C. Huang, P. Zhang, and J. Zhang. Microblogging after a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake. Proceedings of the ACM 2011 conference on Computer Supported Cooperative Work (CSCW), 25--34, Hangzhou, China, March 2011. Google ScholarDigital Library
- T. Rattenbury, N. Good, and M. Naaman. Towards Automatic Extraction of Event and Place Semantics from Flickr Tags. Proceedings of the 30th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '07, 103--110, New York, USA, 2007. Google ScholarDigital Library
- Strehl, J. Ghosh, and C. Cardie. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 583--617, vol. 3, 2002. Google ScholarDigital Library
- T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors. Proceedings of the 19th World Wide Web Conference, 851--860, Raleigh, NC, USA, April 2010. Google ScholarDigital Library
- K. Starbird, L. Palen, A. L. Hughes, and S. Vieweg. Chatter on the Red: What Hazards Threat Reveals about the Social Life of Microblogged Information. Proceedings of the 2010 ACM conference on Computer Supported Cooperative Work (CSCW), 241--250, Savannah, Georgia, USA, February 2010. Google ScholarDigital Library
- C. Torrey, M. Burke, M. Lee, A. Dey, S. Fussell, and S. Kiesler. Connected Giving: Ordinary People Coordinating Disaster Relief on the Internet. Proceedings of Hawaii International Conference on Systems Science 2007, 217, Big Island, USA, January 2007. Google ScholarDigital Library
- S. Vieweg, A. L. Hughes, K. Starbird, and L. Palen. Microblogging during two natural hazards events: What twitter may contribute to situational awareness. Proceedings of the 28th international conference on Human factors in Computing Systems (CHI), 1079--1088, Atlanta, Georgia, USA, April 2010. Google ScholarDigital Library
- S. Verma, S. Vieweg, W. J. Corvey, L. Palen, J. H. Martin, M. Palmer, A. Schram, and K. M. Anderson. Natural Language Processing to the Rescue? Extracting Situational Awareness Tweets During Mass Emergency. Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), 49--57, Barcelona, Spain, July 2011.Google Scholar
- Yin, J., Lampert, A., Cameron, M., Robinson, B., and Power, R. 2012. Using Social Media to Enhance Emergency Situation Awareness. In IEEE Intelligent Systems, vol. 99, PrePrints, February 2012. Google ScholarDigital Library
Index Terms
- Sub-event detection during natural hazards using features of social media data
Recommendations
Automatic sub-event detection in emergency management using social media
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide WebEmergency management is about assessing critical situations, followed by decision making as a key step. Clearly, information is crucial in this two-step process. The technology of social (multi)media turns out to be an interesting source for collecting ...
Sub-event discovery and retrieval during natural hazards on social media data
Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount of social media data, an important task is to discover and retrieve sub-events over time. ...
Automatic Identification of Crisis-Related Sub-events Using Clustering
ICMLA '12: Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis ...
Comments