The DARPA Twitter Bot Challenge
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

TL;DR
The paper discusses DARPA's 2015 Twitter Bot Challenge, focusing on identifying influence bots on specific topics, highlighting the methods of top teams and the importance of ground truth in detection accuracy.
Contribution
It introduces the DARPA Twitter Bot Challenge and details the approaches of the top teams for influence bot detection on targeted topics.
Findings
Top teams employed diverse detection methods.
Ground truth data was crucial for evaluating accuracy.
The challenge advanced influence bot identification techniques.
Abstract
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]).…
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