Recommending Links to Control Elections via Social Influence

Abstract

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.

Keywords

• computational social choice
• election control
• influence maximization
• social networks
• graph augmentation
• approximation algorithms