Detecting and Mitigating Bias in Algorithms Used to Disseminate Information in Social Networks

TL;DR

This paper reveals that influence maximization algorithms in social networks tend to create information inequalities and proposes a multi-objective method to promote both influence and equity, reducing disparities without significantly sacrificing spread.

Contribution

It introduces a novel multi-objective algorithm that balances influence maximization with information equity in social networks.

Findings

Existing algorithms create information gaps and inequalities.

The proposed method reduces disparities with minimal impact on influence spread.

Results are validated through extensive simulations on real-world networks.

Abstract

Social connections are conduits through which individuals communicate, information propagates, and diseases spread. Identifying individuals who are more likely to adopt ideas and spread them is essential in order to develop effective information campaigns, maximize the reach of resources, and fight epidemics. Influence maximization algorithms are used to identify sets of influencers. Based on extensive computer simulations on synthetic and ten diverse real-world social networks we show that seeding information using these methods creates information gaps. Our results show that these algorithms select influencers who do not disseminate information equitably, threatening to create an increasingly unequal society. To overcome this issue we devise a multi-objective algorithm which maximizes influence and information equity. Our results demonstrate it is possible to reduce vulnerability at a relatively low trade-off with respect to spread. This highlights that in our search for maximizing information we do not need to compromise on information equality.