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Author  |
Muhammad Imran; Prasenjit Mitra; Jaideep Srivastava |
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Title |
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages |
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Conference Article |
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Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
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Keywords |
Social Media; Tweets Classification; Domain Adaptation |
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Abstract |
Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased. |
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Publisher |
Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
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Language |
English |
Summary Language |
English |
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Edition |
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ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
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Track |
Social Media Studies |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1396 |
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Author |
Cornelia Caragea; Nathan McNeese; Anuj Jaiswal; Greg Traylor; Hyun-Woo Kim; Prasenjit Mitra; Dinghao Wu; Andrea H. Tapia; Lee Giles; Bernard J. Jansen; John Yen |
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Title |
Classifying text messages for the haiti earthquake |
Type |
Conference Article |
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Year |
2011 |
Publication |
8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 |
Abbreviated Journal |
ISCRAM 2011 |
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Keywords |
Abstracting; Artificial intelligence; Disaster prevention; Information systems; Learning systems; Text processing; Disaster relief; Emergency response; Emergency situation; Haiti earthquakes; Information technology infrastructure; Nongovernmental organizations; Relief workers; Self-organizing behavior; Earthquakes |
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Abstract |
In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims' requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them. |
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Address |
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA-16801, United States |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
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Language |
English |
Summary Language |
English |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9789724922478 |
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Track |
Analytical Information Systems |
Expedition |
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Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
371 |
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