TY - GEN
T1 - Extracting information nuggets from disaster- Related messages in social media
AU - Imran, Muhammad
AU - Elbassuoni, Shady
AU - Castillo, Carlos
AU - Diaz, Fernando
AU - Meier, Patrick
PY - 2013
Y1 - 2013
N2 - Microblogging sites such as Twitter can play a vital role in spreading information during "natural" or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable "information nuggets", brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
AB - Microblogging sites such as Twitter can play a vital role in spreading information during "natural" or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable "information nuggets", brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
KW - Information extraction
KW - Social media
KW - Supervised classification
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84905650414&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84905650414
SN - 9783923704804
T3 - ISCRAM 2013 Conference Proceedings - 10th International Conference on Information Systems for Crisis Response and Management
SP - 791
EP - 801
BT - ISCRAM 2013 Conference Proceedings - 10th International Conference on Information Systems for Crisis Response and Management
PB - Karlsruher Institut fur Technologie (KIT)
T2 - 10th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2013
Y2 - 12 May 2013 through 15 May 2013
ER -