Paper Info
Title
Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies
Abstract
Social media platforms have been contributing to disaster management during the past several years. Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes, such as caution and advice, to better understand the meaning and leverage useful information from the social media text content. However, these methods are mostly event specific and difficult to generalize for cross-event classifications. In other words, traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event. This research examines the capability of a convolutional neural network (CNN) model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy, Harvey, and Irma. The performance of the CNN model is compared to two traditional machine learning methods: support vector machine (SVM) and logistic regression (LR). Experiment results showed that CNN models achieved a consistently better accuracy for both single event and cross-event evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results. This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.
Year
DOI
Venue
2019
10.1080/17538947.2019.1574316
INTERNATIONAL JOURNAL OF DIGITAL EARTH
Keywords
DocType
Volume
Text mining,deep learning,hurricanes,Twitter,convolutional neural network,situational awareness
Journal
12.0
Issue
ISSN
Citations 
SP11.0
1753-8947
2
PageRank 
References 
Authors
0.36
16
5
Name
Order
Citations
PageRank
Manzhu Yu1496.41
Qunying Huang242029.69
Han Qin381.84
Chris Scheele420.36
Chaowei Yang584156.47