Journal article 487 views 90 downloads
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster
Information Systems Frontiers, Volume: 25, Issue: 4, Pages: 1589 - 1604
Swansea University Author:
Yogesh Dwivedi
-
PDF | Accepted Manuscript
Download (413.51KB)
DOI (Published version): 10.1007/s10796-022-10309-x
Abstract
During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by th...
Published in: | Information Systems Frontiers |
---|---|
ISSN: | 1387-3326 1572-9419 |
Published: |
Springer Science and Business Media LLC
2023
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa60323 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
first_indexed |
2022-06-25T19:46:52Z |
---|---|
last_indexed |
2023-01-13T19:20:22Z |
id |
cronfa60323 |
recordtype |
SURis |
fullrecord |
<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>60323</id><entry>2022-06-25</entry><title>Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster</title><swanseaauthors><author><sid>d154596e71b99ad1285563c8fdd373d7</sid><ORCID>0000-0002-5547-9990</ORCID><firstname>Yogesh</firstname><surname>Dwivedi</surname><name>Yogesh Dwivedi</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-06-25</date><deptcode>BBU</deptcode><abstract>During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models.</abstract><type>Journal Article</type><journal>Information Systems Frontiers</journal><volume>25</volume><journalNumber>4</journalNumber><paginationStart>1589</paginationStart><paginationEnd>1604</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1387-3326</issnPrint><issnElectronic>1572-9419</issnElectronic><keywords>Disaster; Eyewitness tweets; Informative contents; Multi-channel convolutional neural network; Recurrent neural network</keywords><publishedDay>1</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-08-01</publishedDate><doi>10.1007/s10796-022-10309-x</doi><url>http://dx.doi.org/10.1007/s10796-022-10309-x</url><notes/><college>COLLEGE NANME</college><department>Business</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BBU</DepartmentCode><institution>Swansea University</institution><apcterm>Other</apcterm><funders/><projectreference/><lastEdited>2023-09-05T12:56:16.4350139</lastEdited><Created>2022-06-25T20:44:39.4356829</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>Abhinav</firstname><surname>Kumar</surname><orcid>0000-0001-9367-7069</orcid><order>1</order></author><author><firstname>Jyoti Prakash</firstname><surname>Singh</surname><order>2</order></author><author><firstname>Nripendra P.</firstname><surname>Rana</surname><order>3</order></author><author><firstname>Yogesh</firstname><surname>Dwivedi</surname><orcid>0000-0002-5547-9990</orcid><order>4</order></author></authors><documents><document><filename>60323__24396__3b2992638f01412fb57a83516ed49165.pdf</filename><originalFilename>Eyewitness_ISF_R5_English_Proofread.pdf</originalFilename><uploaded>2022-06-25T20:46:40.2399241</uploaded><type>Output</type><contentLength>423437</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2023-07-15T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807> |
spelling |
v2 60323 2022-06-25 Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2022-06-25 BBU During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Journal Article Information Systems Frontiers 25 4 1589 1604 Springer Science and Business Media LLC 1387-3326 1572-9419 Disaster; Eyewitness tweets; Informative contents; Multi-channel convolutional neural network; Recurrent neural network 1 8 2023 2023-08-01 10.1007/s10796-022-10309-x http://dx.doi.org/10.1007/s10796-022-10309-x COLLEGE NANME Business COLLEGE CODE BBU Swansea University Other 2023-09-05T12:56:16.4350139 2022-06-25T20:44:39.4356829 Faculty of Humanities and Social Sciences School of Management - Business Management Abhinav Kumar 0000-0001-9367-7069 1 Jyoti Prakash Singh 2 Nripendra P. Rana 3 Yogesh Dwivedi 0000-0002-5547-9990 4 60323__24396__3b2992638f01412fb57a83516ed49165.pdf Eyewitness_ISF_R5_English_Proofread.pdf 2022-06-25T20:46:40.2399241 Output 423437 application/pdf Accepted Manuscript true 2023-07-15T00:00:00.0000000 true eng |
title |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
spellingShingle |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster Yogesh Dwivedi |
title_short |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
title_full |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
title_fullStr |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
title_full_unstemmed |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
title_sort |
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster |
author_id_str_mv |
d154596e71b99ad1285563c8fdd373d7 |
author_id_fullname_str_mv |
d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi |
author |
Yogesh Dwivedi |
author2 |
Abhinav Kumar Jyoti Prakash Singh Nripendra P. Rana Yogesh Dwivedi |
format |
Journal article |
container_title |
Information Systems Frontiers |
container_volume |
25 |
container_issue |
4 |
container_start_page |
1589 |
publishDate |
2023 |
institution |
Swansea University |
issn |
1387-3326 1572-9419 |
doi_str_mv |
10.1007/s10796-022-10309-x |
publisher |
Springer Science and Business Media LLC |
college_str |
Faculty of Humanities and Social Sciences |
hierarchytype |
|
hierarchy_top_id |
facultyofhumanitiesandsocialsciences |
hierarchy_top_title |
Faculty of Humanities and Social Sciences |
hierarchy_parent_id |
facultyofhumanitiesandsocialsciences |
hierarchy_parent_title |
Faculty of Humanities and Social Sciences |
department_str |
School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management |
url |
http://dx.doi.org/10.1007/s10796-022-10309-x |
document_store_str |
1 |
active_str |
0 |
description |
During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. |
published_date |
2023-08-01T12:56:18Z |
_version_ |
1776198592126517248 |
score |
11.021648 |