# Controlling Elections through Social Influence

arXiv: https://arxiv.org/abs/1711.08615 · HTML: https://mlsanity.com/paper/1711.08615/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Majority Opinion Diffusion in Social Networks: An Adversarial Approach](https://mlsanity.com/paper/2012.03143/) (2020)
- [A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks](https://mlsanity.com/paper/2204.03297/) (2022)
- [Drawing a Map of Elections](https://mlsanity.com/paper/2504.03809/) (2025)
- [Random Majority Opinion Diffusion: Stabilization Time, Absorbing States, and Influential Nodes](https://mlsanity.com/paper/2302.06760/) (2023)
- [Viral Marketing in Social Networks with Competing Products](https://mlsanity.com/paper/2312.15819/) (2023)
- [Stochastic Multi-round Submodular Optimization with Budget](https://mlsanity.com/paper/2404.13737/) (2024)

## Abstract (arXiv)

Election control considers the problem of an adversary who attempts to tamper with a voting process, in order to either ensure that their favored candidate wins (constructive control) or another candidate loses (destructive control). As online social networks have become significant sources of information for potential voters, a new tool in an attacker's arsenal is to effect control by harnessing social influence, for example, by spreading fake news and other forms of misinformation through online social media. We consider the computational problem of election control via social influence, studying the conditions under which finding good adversarial strategies is computationally feasible. We consider two objectives for the adversary in both the constructive and destructive control settings: probability and margin of victory (POV and MOV, respectively). We present several strong negative results, showing, for example, that the problem of maximizing POV is inapproximable for any constant factor. On the other hand, we present approximation algorithms which provide somewhat weaker approximation guarantees, such as bicriteria approximations for the POV objective and constant-factor approximations for MOV. Finally, we present mixed integer programming formulations for these problems. Experimental results show that our approximation algorithms often find near-optimal control strategies, indicating that election control through social influence is a salient threat to election integrity.