Learning from the Crowd: Collaborative Filtering Techniques for Identifying On-the-Ground Twitterers during Mass Disruptions

Kate Starbird, Felix Muzny, Leysia Palen

Abstract:

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.