2016/10/04 by Dat Tien Nguyen, Shafiq Joty, Nguyen, Dat Tien +7
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Public Relations and Crisis Communication #Seismology and Earthquake Studies #Sentiment Analysis and Opinion Mining #cs.CL #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.1610.01030
Accepted at SWDM co-located with CIKM 2016. 6 pages, 2 figures. arXiv admin note: text overlap with arXiv:1608.03902
openalex publication_date 2016/10/04 · arxiv created 2016/10/05 · arxiv updated 2016/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. We test our models using a crisis-related real-world Twitter dataset.