Localized Flood Detection With Minimal Labeled Social Media Data Using Transfer Learning

Thumbnail Image

Files

2003.04973.pdf (647.07 KB)

Links to Files

https://arxiv.org/abs/2003.04973

Permanent Link

http://hdl.handle.net/11603/22233

Collections

UMBC Information Systems Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2020-02-10

Type of Work

conference papers and proceedings preprints
Text

Department

Program

Citation of Original Publication

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Abstract

Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data. This study is important since it can immensely help in providing the flood-related updates and notifications to the city officials for emergency decision making, rescue operations, and early warnings, etc. We propose to perform the text classification using the inductive transfer learning method i.e pre-trained language model ULMFiT and fine-tune it in order to effectively classify the flood-related feeds in any new location. Finally, we show that using very little new labeled data in the target domain we can successfully build an efficient and high performing model for flood detection and analysis with human-generated facts and observations from Twitter.