TY - GEN
T1 - Identifying sub-events and summarizing disaster-related information from microblogs
AU - Rudra, Koustav
AU - Goyal, Pawan
AU - Ganguly, Niloy
AU - Mitra, Prasenjit
AU - Imran, Muhammad
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/6/27
Y1 - 2018/6/27
N2 - In recent times, humanitarian organizations increasingly rely on social media to search for information useful for disaster response. These organizations have varying information needs ranging from general situational awareness (i.e., to understand a bigger picture) to focused information needs e.g., about infrastructure damage, urgent needs of affected people. This research proposes a novel approach to help crisis responders fulfill their information needs at different levels of granularities. Specifically, the proposed approach presents simple algorithms to identify sub-events and generate summaries of big volume of messages around those events using an Integer Linear Programming (ILP) technique. Extensive evaluation on a large set of real world Twitter dataset shows (a). our algorithm can identify important sub-events with high recall (b). The summarization scheme shows (6 - -30%) higher accuracy of our system compared to many other state-of-the-art techniques. The simplicity of the algorithms ensures that the entire task is done in real time which is needed for practical deployment of the system.
AB - In recent times, humanitarian organizations increasingly rely on social media to search for information useful for disaster response. These organizations have varying information needs ranging from general situational awareness (i.e., to understand a bigger picture) to focused information needs e.g., about infrastructure damage, urgent needs of affected people. This research proposes a novel approach to help crisis responders fulfill their information needs at different levels of granularities. Specifically, the proposed approach presents simple algorithms to identify sub-events and generate summaries of big volume of messages around those events using an Integer Linear Programming (ILP) technique. Extensive evaluation on a large set of real world Twitter dataset shows (a). our algorithm can identify important sub-events with high recall (b). The summarization scheme shows (6 - -30%) higher accuracy of our system compared to many other state-of-the-art techniques. The simplicity of the algorithms ensures that the entire task is done in real time which is needed for practical deployment of the system.
KW - Class-based summarization
KW - High-level summarization
KW - Humanitarian classes
KW - Situational information
KW - Sub-event detection
UR - http://www.scopus.com/inward/record.url?scp=85051464585&partnerID=8YFLogxK
U2 - 10.1145/3209978.3210030
DO - 10.1145/3209978.3210030
M3 - Conference contribution
AN - SCOPUS:85051464585
T3 - 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
SP - 265
EP - 274
BT - 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
PB - Association for Computing Machinery, Inc
T2 - 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
Y2 - 8 July 2018 through 12 July 2018
ER -