2016/01/20 by V. S. Subrahmanian, Amos Azaria, Skylar Durst +18 · 1 voice
Computer Science · Physics and Astronomy · #cs.SI #cs.AI #cs.CY #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1109/mc.2016.183
arxiv published 2016/01/20 · arxiv updated 2016/04/21
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.