Authors: V. S. Subrahmanian,Amos Azaria,Skylar Durst,Vadim Kagan,Aram Galstyan,Kristina Lerman,Linhong Zhu,Emilio Ferrara,Alessandro Flammini,Filippo Menczer,Andrew Stevens,Alexander Dekhtyar,Shuyang Gao,Tad Hogg,Farshad Kooti,Yan Liu,Onur Varol,Prashant Shiralkar,Vinod Vydiswaran,Qiaozhu Mei,Tim Hwang
ArXiv: 1601.05140
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DOI
Abstract URL: http://arxiv.org/abs/1601.05140v2
A number of organizations ranging from terrorist groups such as ISIS to
politicians and nation states reportedly conduct explicit campaigns to
influence opinion on social media, posing a risk to democratic processes. There
is thus a growing need to identify and eliminate "influence bots" - realistic,
automated identities that illicitly shape discussion on sites like Twitter and
Facebook - before they get too influential. Spurred by such events, DARPA held
a 4-week competition in February/March 2015 in which multiple teams supported
by the DARPA Social Media in Strategic Communications program competed to
identify a set of previously identified "influence bots" serving as ground
truth on a specific topic within Twitter. Past work regarding influence bots
often has difficulty supporting claims about accuracy, since there is limited
ground truth (though some exceptions do exist [3,7]). However, with the
exception of [3], no past work has looked specifically at identifying influence
bots on a specific topic. This paper describes the DARPA Challenge and
describes the methods used by the three top-ranked teams.