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Who Moderates the Moderators? Crowdsourcing Abuse Detection in Usergenerated Content
"... A large fraction of usergenerated content on the Web, such as posts or comments on popular online forums, consists of abuse or spam. Due to the volume of contributions on popular sites, a few trusted moderators cannot identify all such abusive content, so viewer ratings of contributions must be use ..."
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A large fraction of usergenerated content on the Web, such as posts or comments on popular online forums, consists of abuse or spam. Due to the volume of contributions on popular sites, a few trusted moderators cannot identify all such abusive content, so viewer ratings of contributions must be used for moderation. But not all viewers who rate content are trustworthy and accurate. What is a principled approach to assigning trust and aggregating user ratings, in order to accurately identify abusive content? In this paper, we introduce a framework to address the problem of moderating online content using crowdsourced ratings. Our framework encompasses users who are untrustworthy or inaccurate to an unknown extent — that is, both the content and the raters are of unknown quality. With no knowledge whatsoever about the raters, it is impossible to do better than a random estimate. We present efficient algorithms to accurately detect abuse that only require knowledge about the identity of a single ‘good ’ agent, who rates contributions accurately more than half the time. We prove that our algorithm can infer the quality of contributions with error that rapidly converges to zero as the number of observations increases; we also numerically demonstrate that the algorithm has very high accuracy for much fewer observations. Finally, we analyze the robustness of our algorithms to manipulation by adversarial or strategic raters, an important issue in moderating online content, and quantify how the performance of the algorithm degrades with the number of manipulating agents. 1.
On the computation of fully proportional representation
 JOURNAL OF AI RESEARCH
, 2013
"... We investigate two systems of fully proportional representation suggested by Chamberlin & Courant and Monroe. Both systems assign a representative to each voter so that the “sum of misrepresentations” is minimized. The winner determination problem for both systems is known to be NPhard, hence t ..."
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Cited by 18 (7 self)
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We investigate two systems of fully proportional representation suggested by Chamberlin & Courant and Monroe. Both systems assign a representative to each voter so that the “sum of misrepresentations” is minimized. The winner determination problem for both systems is known to be NPhard, hence this work aims at investigating whether there are variants of the proposed rules and/or specific electorates for which these problems can be solved efficiently. As a variation of these rules, instead of minimizing the sum of misrepresentations, we considered minimizing the maximalmisrepresentationintroducingeffectively two new rules. In the general case these “minimax ” versions of classical rules appeared to be still NPhard. We investigated the parameterized complexity of winner determination of the two classical and two new rules with respect to several parameters. Here we have a mixture of positive and negative results: e.g., we proved fixedparameter tractability for the parameter the number of candidates but fixedparameter intractability for the number of winners. For singlepeaked electorates our results are overwhelmingly positive: we provide polynomialtime algorithms for most of the considered problems. The only rule that remains NPhard for singlepeaked electorates is the classical Monroe rule. 1.
The price of neutrality for the ranked pairs method
 In Proc. of AAAI2012
, 2012
"... The complexity of the winner determination problem has been studied for almost all common voting rules. A notable exception, possibly caused by some confusion regarding its exact definition, is the method of ranked pairs. The original version of the method, due to Tideman, yields a social preference ..."
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The complexity of the winner determination problem has been studied for almost all common voting rules. A notable exception, possibly caused by some confusion regarding its exact definition, is the method of ranked pairs. The original version of the method, due to Tideman, yields a social preference function that is irresolute and neutral. A variant introduced subsequently uses an exogenously given tiebreaking rule and therefore fails neutrality. The latter variant is the one most commonly studied in the area of computational social choice, and it is easy to see that its winner determination problem is computationally tractable. We show that by contrast, computing the set of winners selected by Tideman’s original ranked pairs method is NPcomplete, thus revealing a tradeoff between tractability and neutrality. In addition, several known results concerning the hardness of manipulation and the complexity of computing possible and necessary winners are shown to follow as corollaries from our findings. 1
Computational Aspects of MultiWinner Approval Voting
 In Proceedings of the 8th Multidisciplinary Workshop on Advances in Preference Handling. 7
, 2014
"... We study computational aspects of three prominent voting rules that use approval ballots to select multiple winners. These rules are proportional approval voting, reweighted approval voting, and satisfaction approval voting. Each rule is designed with the intention to compute a representative winni ..."
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We study computational aspects of three prominent voting rules that use approval ballots to select multiple winners. These rules are proportional approval voting, reweighted approval voting, and satisfaction approval voting. Each rule is designed with the intention to compute a representative winning set. We show that computing the winner for proportional approval voting is NPhard, closing an open problem (Kilgour, 2010). As none of the rules we examine are strategyproof, we study various strategic aspects of the rules. In particular, we examine the computational complexity of computing a best response for both a single agent and a group of agents. In many settings, we show that it is NPhard for an agent or agents to compute how best to vote given a fixed set of approval ballots of the other agents.
Using Mechanism Design to Prevent FalseName Manipulations
"... When mechanisms such as auctions, rating systems, and elections are run in a highly anonymous environment such as the Internet, a key concern is that a single agent can participate multiple times by using false identifiers. Such falsename manipulations have traditionally not been considered in the ..."
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Cited by 8 (4 self)
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When mechanisms such as auctions, rating systems, and elections are run in a highly anonymous environment such as the Internet, a key concern is that a single agent can participate multiple times by using false identifiers. Such falsename manipulations have traditionally not been considered in the theory of mechanism design. In this article, we review recent efforts to extend the theory to address this. We first review results for the basic concept of falsenameproofness. Because some of these results are very negative, we also discuss alternative models that allow us to circumvent some of these negative results. Technologies such as the Internet allow many spatially distributed parties (or agents) to rapidly interact according to intricate protocols. Some of the most exciting applications
Control complexity in Bucklin and fallback voting
 Computing Research Repository
, 2011
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Parameterized algorithmics for computational social choice: nine research challenges
 Tsinghua Science and Technology
, 2014
"... Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in ..."
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Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multiagent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problemspecific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context.
Computing Optimal Outcomes under an Expressive Representation of Settings with Externalities
, 2005
"... When a decision must be made based on the preferences of multiple agents, and the space of possible outcomes is combinatorial in nature, it becomes necessary to think about how preferences should be represented, and how this affects the complexity of finding an optimal (or at least a good) outcome. ..."
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When a decision must be made based on the preferences of multiple agents, and the space of possible outcomes is combinatorial in nature, it becomes necessary to think about how preferences should be represented, and how this affects the complexity of finding an optimal (or at least a good) outcome. We study settings with externalities, where each agent controls one or more variables, and how these variables are set affects not only the agent herself, but also potentially the other agents. For example, one agent may decide to reduce her pollution, which will come at a cost to herself, but will result in a benefit for all other agents. We formalize how to represent such domains and show that in a number of key special cases, it is NPcomplete to determine whether there exists a nontrivial feasible solution (and therefore the maximum social welfare is completely inapproximable). However, for one important special case, we give an algorithm that converges to the solution with the maximal concession by each agent (in a linear number of rounds for utility functions that additively decompose into piecewise constant functions). Maximizing social welfare, however, remains NPhard even in this setting. We also demonstrate a special case that can be solved in polynomial time using linear programming.
Comparing Multiagent Systems Research in Combinatorial Auctions and Voting
"... In a combinatorial auction, a set of resources is for sale, and agents can bid on subsets of these resources. In a voting setting, the agents decide among a set of alternatives by having each agent rank all the alternatives. Many of the key research issues in these two domains are similar. The aim o ..."
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Cited by 4 (2 self)
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In a combinatorial auction, a set of resources is for sale, and agents can bid on subsets of these resources. In a voting setting, the agents decide among a set of alternatives by having each agent rank all the alternatives. Many of the key research issues in these two domains are similar. The aim of this paper is to give a convenient sidebyside comparison that will clarify the relation between the domains, and serve as a guide to future research.