TY - GEN
T1 - Coordinating human and machine intelligence to classify microblog communications in crises
AU - Imran, Muhammad
AU - Castillo, Carlos
AU - Lucas, Ji
AU - Meier, Patrick
AU - Rogstadius, Jakob
PY - 2014
Y1 - 2014
N2 - An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
AB - An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
UR - http://www.scopus.com/inward/record.url?scp=84905845537&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84905845537
SN - 9780692211946
T3 - ISCRAM 2014 Conference Proceedings - 11th International Conference on Information Systems for Crisis Response and Management
SP - 712
EP - 721
BT - ISCRAM 2014 Conference Proceedings - 11th International Conference on Information Systems for Crisis Response and Management
PB - The Pennsylvania State University
T2 - 11th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2014
Y2 - 1 May 2014 through 1 May 2014
ER -