Opinion Diffusion and Campaigning on Society Graphs

Faliszewski, Piotr; Gonen, Rica; Koutecký, Martin; Talmon, Nimrod

doi:10.24963/ijcai.2018/30

Cited by 39 publications

(29 citation statements)

References 33 publications

(72 reference statements)

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“…They have shown that the problem can be approximated within the same bound. A similar problem has been studied in [13]. The authors consider a network where each node is a set of voters with the same preference list, and edges connect nodes whose preference lists differ by the ordering of a single pair of adjacent candidates.…”

Section: Related Workmentioning

confidence: 99%

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%

Multi-winner Election Control via Social Influence

Mehrizi

D’Angelo

2020

Structural Information and Communication Complexity

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“…In both cases, the election control problem can be solved with an approximation factor of 1 3 (1 − 1/e) in the constructive scenario and 1 2 (1 − 1/e) in the destructive one. Moreover, Faliszewski et al [11] studied a variant of the Linear Threshold Model with weighted vertices; in their scenario, each node of the graph is a group of voters with a specific list of candidates and there is an edge between two nodes if they differ by the ordering of a single pair of adjacent candidates. Bredereck et al [12] instead focused on a simple Linear Threshold Model, where each node holds a binary opinion, each edge has the same fixed weight, and all vertices have a threshold fixed to 1/2; in this scenario, they studied how to manipulate, by means of bribing nodes or adding/deleting edges, the network in order to have control on the majority opinion, i.e., having more than 50% of nodes with the same opinion.…”

Section: Related Workmentioning

confidence: 99%

Recommending Links to Control Elections via Social Influence

2019

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Political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, for example by enlarging their number of connections in the social network in order to assure a larger portion of users. Indeed, adding new connections between users increases the capabilities of a social network of spreading information, which in turn can increase the retention rate and the number of new voters. In this work, we address the problem of selecting a fixed-size set of new connections to be added to a subset of voters that, with their influence, will change the opinion of the network’s users about a target candidate, maximizing its chances to win the election. We provide a constant factor approximation algorithm for this problem and we experimentally show that, with few new links and small computational time, our algorithm is able to maximize the chances to make the target candidate win the elections.

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“…A number of recent works study social influence as a means of election control (Sina et al 2015;Faliszewski et al 2018;Wilder and Vorobeychik 2018;. The crucial difference in our model is that voters are strategic players, who update their beliefs rationally.…”

Section: Introductionmentioning

confidence: 99%

Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud

Castiglioni

Celli

Gatti

2020

AAAI

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We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We resort to the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and focus on, as illustrative examples, k-voting rules and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a k-voting rule and plurality voting. In establishing these results, we provide two contributions applicable to general settings beyond voting. Specifically, we extend a well-known result by Dughmi and Xu (2017) to more general settings and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed-parameter tractable in the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a k-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous.

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Opinion Diffusion and Campaigning on Society Graphs

Cited by 39 publications

References 33 publications

Multi-winner Election Control via Social Influence

Multi-winner Election Control via Social Influence

Recommending Links to Control Elections via Social Influence

Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud

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