@article{1780,
  author = {Hua Bai, Xunguo Lin, Bella Robinsion, Robert Power},
  title = {Sina Weibo Incident Monitor and Chinese Disaster Microblogging Classification},
  journal = {Journal of Digital Information Management},
  year = {2015},
  volume = {13},
  number = {3},
  doi = {},
  url = {},
  abstract = {This paper describes the initial work on
developing an all-hazards emergency event detector using messages obtained in near-real-time from the public timeline of the Chinese Sina Weibo microblogging service. The system filters target keywords corresponding to
emergency events of earthquakes, floods, typhoons, fires and storms and then uses classifiers to identify messages
from people experiencing the corresponding emergency event. Then, this study carried out experiments that compare the performance of four different classification
methods and also explore to improve the classifier by the new training data captured by SWIM recently. After Chinese text pre-processing, feature selection and training
set size, the experimental results demonstrate Random forests classifier could get best performance but need more long time to run in R, thus the potential to improve this classifier for setting up the SWIM system need to be explored in the future. While similar work has been reported using Twitter content, this is the first time these techniques have been applied to the Sina Weibo
microblogging service for multiple emergency event types. This paper also outline the experience of accessing Sina
Weibo messages, provide a summary of their structure and content, note the challenges faced in processing this text using Natural Language Processing packages and
outline the developed website for users to view the processed messages. The long term aim is to develop a general emergency notification and monitoring system for various disaster event types in China reported by the public on Sina Weibo which can be used by the appropriate
emergency services as a source of improved situational awareness.},
}