The DARPA Twitter Bot Challenge. Subrahmanian, V. S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., Menczer, F., Waltzman, R., Stevens, A., Dekhtyar, A., Gao, S., Hogg, T., Kooti, F., Liu, Y., Varol, O., Shiralkar, P., Vydiswaran, V., Mei, Q., & Huang, T. IEEE Computer Magazine, 2016.
The DARPA Twitter Bot Challenge [link]Paper  abstract   bibtex   8 downloads  
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.
@article{darpabotchallenge,
    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]). 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.},
    author = {Subrahmanian, V. S. and Azaria, Amos and Durst, Skylar and Kagan, Vadim and Galstyan, Aram and Lerman, Kristina and Zhu, Linhong and Ferrara, Emilio and Flammini, Alessandro and Menczer, Filippo and Waltzman, Rand and Stevens, Andrew and Dekhtyar, Alexander and Gao, Shuyang and Hogg, Tad and Kooti, Farshad and Liu, Yan and Varol, Onur and Shiralkar, Prashant and Vydiswaran, Vinod and Mei, Qiaozhu and Huang, Tim},
    journal = {IEEE Computer Magazine},
    title = {The {DARPA} Twitter Bot Challenge},
    url = {http://arxiv.org/abs/1601.05140},
    year = {2016}
}
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