Mitigating Overexposure in Viral Marketing

TL;DR

This paper develops a theoretical model to optimize viral marketing strategies that minimize overexposure, balancing reach and negative reputation effects, and demonstrates its effectiveness through simulations on real networks.

Contribution

It introduces a novel theoretical framework for viral marketing that accounts for overexposure risks and provides a polynomial-time algorithm for optimal strategy computation.

Findings

Optimal strategies outperform natural baselines in simulations

The model captures key phenomena of overexposure effects

Polynomial-time algorithm enables practical application

Abstract

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines