Exploiting Social Influence to Control Elections Based on Scoring Rules

Coró, Federico; Cruciani, Emilio; D’Angelo, Gianlorenzo; Ponziani, Stefano

doi:10.24963/ijcai.2019/29

Cited by 23 publications

(12 citation statements)

References 19 publications

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“…Wilder and Vorobeychik used the Margin of Victory (MoV) as an objective function and showed that there exists a greedy algorithm that approximates an optimal solution by a factor 1/3(1 − 1/e) for constructive and 1/2(1−1/e) for the destructive case. The same problem has been extended to LTM and general scoring rules [26] by Corò et al [10,11]. They have shown that the problem can be approximated within the same bound.…”

Section: Related Workmentioning

confidence: 99%

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Multi-winner Election Control via Social Influence

Mehrizi

D’Angelo

2020

Structural Information and Communication Complexity

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The extensive use of social media in political campaigns has motivated the recent study of election control problem in social networks.In an election, we are given a set of voters, each having a preference list over a set of candidates, that are distributed on a social network. The winners of the election are computed by aggregating the preference lists of voters according to a so-called voting rule. We consider a scenario where voters may change their preference lists as a consequence of the messages received by their neighbors in a social network. Specifically, we consider a political campaign that spreads messages in a social network in support or against a given candidate and the spreading follows a dynamic model for information diffusion. When a message reaches a voter, this latter changes its preference list according to an update rule. The election control problem asks to find a bounded set of nodes to be the starter of a political campaign in support (constructive problem) or against (destructive problem) a given target candidate c, in such a way that the margin of victory of c w.r.t. its most voted opponents is maximized. It has been shown that several variants of the problem can be solved within a constant factor approximation of the optimum, which shows that controlling elections by means of social networks is doable and constitutes a real problem for modern democracies. Most of the literature, however, focuses on the case of single-winner elections.In this paper, we define the election control problem in social networks for multi-winner elections with the aim of modeling parliamentarian elections. Differently from the single-winner case, we show that the multi-winner election control problem is NP -hard to approximate within any factor in both constructive and destructive cases. We then study a relaxation of the problem where votes are aggregated on the basis of parties (instead of single candidates), which is a variation of the so-called straight-party voting used in some real parliamentarian elections. We show that the latter problem remains NP -hard but can be approximated within a constant factor.

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Section: Related Workmentioning

confidence: 99%

“…The above rule for updating the preference lists is commonly used in the literature [10,25]. In this model, we consider just one message, which contains some positive/negative information about the target party that will affect all the target candidates.…”

Section: Multi-winner Election Controlmentioning

confidence: 99%

Multi-winner Election Control via Social Influence

Mehrizi

D’Angelo

2020

Structural Information and Communication Complexity

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“…The authors provide approximation algorithms for plurality voting when the objective function is the maximization of the margin of victory. These approximation results also hold when other voting rules and/or other diffusion models are adopted, as showed by Corò et al (2019bCorò et al ( , 2019a. While the works mentioned above assume that the manipulator has complete knowledge about the problem, some recent work also deals with uncertainty on the network (Abouei Mehrizi et al, 2020).…”

Section: Related Workmentioning

confidence: 72%

“…The works by Wilder and Vorobeychik (2018) and Corò et al (2019bCorò et al ( , 2019a present some limitations when dealing with elections with more than two candidates. A major limitation is the assumption that all the seeds send the same information, and this information is on a single candidate.…”

Section: Related Workmentioning

confidence: 99%

Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

Castiglioni

Ferraioli

Gatti

et al. 2021

jair

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We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases. More precisely, in the case of seeding, we show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to election manipulation when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges, and to the basic case of social influence maximization/minimization in the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold in a re-optimization variant, where the manipulator already knows an optimal solution to the problem and computes a new solution once a local modification occurs, e.g., the removal/addition of a single edge.

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“…Only recently, the setting with multiple candidates has been investigated. In particular, Wilder and Vorobeychik (2018) study the manipulation of a plurality voting based election when the information spread in the network according to an Independent Cascade model; Corò et al (2019) extend this result to a linear threshold diffusion model and arbitrary scoring rules; and Aboueimehrizi et al (2019) allow a manipulator to have imperfect information on the network. However, all of these works still assume the manipulator only sends messages in favour or against a single candidate.…”

Section: Introductionmentioning

confidence: 99%

Election Control in Social Networks via Edge Addition or Removal

Castiglioni

Ferraioli

Gatti

2020

AAAI

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We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

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Exploiting Social Influence to Control Elections Based on Scoring Rules

Cited by 23 publications

References 19 publications

Multi-winner Election Control via Social Influence

Multi-winner Election Control via Social Influence

Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

Election Control in Social Networks via Edge Addition or Removal

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