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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.Comment: IEEE Computer Magazine, in pres

A general method for combining existing algorithms into new programs that are unequivocally preferable to any of the component algorithms is presented. This method, based on notions of risk in economics, offers a computational portfolio design procedure that can be used for a wide range of problems. Tested by solving a canonical NP-complete problem, the method can be used for problems ranging from the combinatorics of DNA sequencing to the completion of tasks in environments with resource contention, such as the World Wide Web.

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity a priori, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.

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We report the realization of a nuclear magnetic resonance computer with three quantum bits that simulates an adiabatic quantum optimization algorithm. Adiabatic quantum algorithms offer new insight into how quantum resources can be used to solve hard problems. This experiment uses a particularly well suited three quantum bit molecule and was made possible by introducing a technique that encodes general instances of the given optimization problem into an easily applicable Hamiltonian. Our results indicate an optimal run time of the adiabatic algorithm that agrees well with the prediction of a simple decoherence model.Since the discovery of Shor's[1] and Grover's [2] algorithms, the quest of finding new quantum algorithms proved a formidable challenge. Recently however, a novel algorithm was proposed, using adiabatic evolution [3,4]. Despite the uncertainty in its scaling behavior, this algorithm remains a remarkable discovery because it offers new insights into the potential usefulness of quantum resources for computational tasks.Experimental realizations of quantum algorithms in the past demonstrated Grover's search algorithm [5,6], the Deutsch-Jozsa algorithm [7,8,9], order-finding [10], and Shor's algorithm [11]. Recently, Hogg's algorithm was implemented using only one computational step [12], however a demonstration of an adiabatic quantum algorithm thus far has remained beyond reach.Here, we provide the first experimental implementation of an adiabatic quantum optimization algorithm using three qubits and nuclear magnetic resonance (NMR) techniques [13,14]. NMR techniques are especially attractive because several tens of qubits may be accessible, which is precisely the range that could be crucial in determining the scaling behavior of adiabatic quantum algorithms [15]. Compared to earlier implementations of search problems [5,6], this experiment is a full implementation of a true optimization problem, which does not require a black box function or ancilla bits.This experiment was made possible by overcoming two experimental challenges. First, an adiabatic evolution requires a smoothly varying Hamiltonian over time, but the terms of the available Hamiltonian in our system cannot be smoothly varied and may even have fixed values. We developed a method to approximately smoothly vary a Hamiltonian despite the given restrictions by extending NMR average Hamiltonian techniques [16]. Second, general instances of the optimization algorithm may require the application of Hamiltonians that are not easily accessible. We developed methods to implement general instances of a well known classical NP-complete optimization problem given a fixed natural system Hamiltonian.We provide a concrete procedure detailing these methods. We then apply the results to our optimization problem which is known as Maximum Cut or maxcut [17]. Our experimental results indicate there exists an optimal total running time which can be predicted using a decoherence model that is based on independent stochastic relaxation of the spins. The evolut...

With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.

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