TY - GEN
T1 - AIDR
T2 - 23rd International Conference on World Wide Web, WWW 2014
AU - Imran, Muhammad
AU - Castillo, Carlos
AU - Lucas, Ji
AU - Meier, Patrick
AU - Vieweg, Sarah
N1 - Publisher Copyright:
© Copyright 2014 by the International World Wide Web Conferences Steering Committee.
PY - 2014/4/7
Y1 - 2014/4/7
N2 - We present AIDR (Artificial Intelligence for Disaster Re- sponse), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply hu- man intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that peo- ple post during disasters into a set of user-defined categories of information (e.g., \needs", \damage", etc.) For this pur- pose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification tech- niques) and leverages human-participation (through crowd- sourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted dur- ing the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
AB - We present AIDR (Artificial Intelligence for Disaster Re- sponse), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply hu- man intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that peo- ple post during disasters into a set of user-defined categories of information (e.g., \needs", \damage", etc.) For this pur- pose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification tech- niques) and leverages human-participation (through crowd- sourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted dur- ing the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
KW - Classification
KW - Crowdsourcing
KW - Online machine learning
KW - Stream processing
UR - http://www.scopus.com/inward/record.url?scp=84905843654&partnerID=8YFLogxK
U2 - 10.1145/2567948.2577034
DO - 10.1145/2567948.2577034
M3 - Conference contribution
AN - SCOPUS:84905843654
T3 - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
SP - 159
EP - 162
BT - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
Y2 - 7 April 2014 through 11 April 2014
ER -