# Exploiting Social Influence to Control Elections Based on Scoring Rules

## TL;DR

This paper introduces a new model for influencing voter opinions in social networks to control election outcomes, providing approximation algorithms and validating them through experiments on real-world data.

## Contribution

It proposes the Linear Threshold Ranking model and develops approximation algorithms for election control under various scoring rules.

## Key findings

- Achieves a $1-1/e$ approximation for maximizing candidate score.
- Provides a $1/3(1-1/e)$-approximation for constructive control.
- Validates the model and algorithms with experiments on real social network data.

## Abstract

Online social networks are used to diffuse opinions and ideas among users, enabling a faster communication and a wider audience. The way in which opinions are conditioned by social interactions is usually called social influence. Social influence is extensively used during political campaigns to advertise and support candidates. Herein we consider the problem of exploiting social influence in a network of voters in order to change their opinion about a target candidate with the aim of increasing his chance to win/lose the election in a wide range of voting systems. We introduce the Linear Threshold Ranking, a natural and powerful extension of the well-established Linear Threshold Model, which describes the change of opinions taking into account the amount of exercised influence. We are able to maximize the score of a target candidate up to a factor of $1-1/e$ by showing submodularity. We exploit such property to provide a $\frac{1}{3}(1-1/e)$-approximation algorithm for the constructive election control problem. Similarly, we get a $\frac{1}{2}(1-1/e)$-approximation ratio in the destructive scenario. The algorithm can be used in arbitrary scoring rule voting systems, including plurality rule and borda count. Finally, we perform an experimental study on real-world networks, measuring Probability of Victory (PoV) and Margin of Victory (MoV) of the target candidate, to validate the model and to test the capability of the algorithm.