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Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground Twitterers during mass disruptions
Kate Starbird
author
Grace Muzny
author
Leysia Palen
author
2012
Simon Fraser University
Vancouver, BC
English
Social media tools, including the microblogging platform Twitter, have been appropriated during mass disruption events by those affected as well as the digitally-convergent crowd. Though tweets sent by those local to an event could be a resource both for responders and those affected, most Twitter activity during mass disruption events is generated by the remote crowd. Tweets from the remote crowd can be seen as noise that must be filtered, but another perspective considers crowd activity as a filtering and recommendation mechanism. This paper tests the hypothesis that crowd behavior can serve as a collaborative filter for identifying people tweeting from the ground during a mass disruption event. We test two models for classifying on-the-ground Twitterers, finding that machine learning techniques using a Support Vector Machine with asymmetric soft margins can be effective in identifying those likely to be on the ground during a mass disruption event. © 2012 ISCRAM.
Artificial intelligence
Information systems
Learning systems
Social networking (online)
Support vector machines
Crisis informatics
Human computation
Mass disruption
Microblogging
Political protest
Behavioral research
exported from refbase (http://idl.iscram.org/show.php?record=208), last updated on Wed, 05 Aug 2015 11:43:12 +0200
text
http://idl.iscram.org/files/starbird/2012/208_Starbird_etal2012.pdf
KateStarbird_etal2012
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management
ISCRAM 2012
L. Rothkrantz
J
Ristvej
editor
9th International ISCRAM Conference on Information Systems for Crisis Response and Management
2012
Simon Fraser University
Vancouver, BC
conference publication
9780864913326
2411-3387
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