TY - GEN
T1 - CRISISDPs
T2 - 16th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2019
AU - Alam, Firoj
AU - Imran, Muhammad
AU - Ofli, Ferda
N1 - Publisher Copyright:
© 2019 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid tasks. However, many technologies are still limited as they require both manual and automatic approaches, and more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we develop automatic data processing services that are freely and publicly available, and made to be simple, efficient, and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform state-of-the-art publicly available tools in terms of classification accuracy.
AB - Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid tasks. However, many technologies are still limited as they require both manual and automatic approaches, and more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we develop automatic data processing services that are freely and publicly available, and made to be simple, efficient, and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform state-of-the-art publicly available tools in terms of classification accuracy.
KW - Application programming interfaces
KW - Data processing services
KW - Humanitarian data processing
KW - Social media
KW - Text classification
UR - http://www.scopus.com/inward/record.url?scp=85077738693&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077738693
T3 - Proceedings of the International ISCRAM Conference
SP - 719
EP - 733
BT - ISCRAM 2019 - Proceedings
A2 - Franco, Zeno
A2 - Gonzalez, Jose J.
A2 - Canos, Jose H.
PB - Information Systems for Crisis Response and Management, ISCRAM
Y2 - 19 May 2019 through 22 May 2019
ER -