Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Connected Giving: Ordinary People Coordinating Disaster Relief on the Internet
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
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
Automatic sub-event detection in emergency management using social media
Proceedings of the 21st international conference companion on World Wide Web
Using Social Media to Enhance Emergency Situation Awareness
IEEE Intelligent Systems
| Hi-index | 0.00 |
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.