Identifying disaster-related tweets and their semantic,spatial and temporal context using deep learning,natural language processing and spatial analysis:a case study of Hurricane Irma  

作　　者：Muhammed Ali Sit Caglar Koylu Ibrahim Demir 

机构地区：[1]Department of Computer Science,University of Iowa,Iowa City,IA,USA [2]Department of Geographical and Sustainability Sciences,University of Iowa,Iowa City,IA,USA [3]Department of Civil and Environmental Engineering,University of Iowa,Iowa City,IA,USA

出　　处：《International Journal of Digital Earth》2019年第11期1205-1229,共25页国际数字地球学报（英文）

摘　　要：We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and time.We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disasterrelatedness.We employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies.Second,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted services.Third,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,respectively.Using Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the disaster.Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.

关 键 词：Social sensing TWITTER deep learning natural language processing spatial analysis HURRICANE 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]