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.
Highlights
Over the past few years, political parties learned that, along with advertising on traditional mediums such as television and newspapers, they must use social media campaigns to defeat their opponents
We provide a (1 − 1/e)-approximation algorithm for the problem of maximizing the score of a target candidate by showing submodularity, where e is the base of the natural logarithm
Since our Algorithm 1 is a modified version of the algorithm G REEDY 2 presented in [25], whose aim was to maximize the influence diffusion in a social network, we can exploit the same reduction to the Limited Seed Selection problem (LSS) that aims at finding a subset of the initial active users in order to maximize the expected number of influenced users
Summary
Over the past few years, political parties learned that, along with advertising on traditional mediums such as television and newspapers, they must use social media campaigns to defeat their opponents. US election where a study shows that 92% percent of people remembered the pro-Trump fake news, and 23% percent of them remembered the pro-Clinton fake news [5] Another example, in 2017, where automated accounts in social networks spread a considerable portion of political-related content, mostly fake news, trying to influence the French election [2]. It is important for a political party to establish and ensure its media presence. And this is the case we study in this paper, they can enlarge their number of connections in the social network in order to assure a larger portion of users. Our goal is to study the case in which an external manipulator can decide to add new links in the network in order to change the outcome and make a specific candidate win
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