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The DARPA Twitter Bot Challenge

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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
Document:  PDF  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.

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