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.

翻译：社会连接是个体沟通、信息传播以及疾病扩散的渠道。为制定有效的信息宣传活动、最大化资源覆盖范围并抗击流行病，识别那些更易接受并传播观点的个体至关重要。影响力最大化算法被用于识别有影响力的群体。基于对合成网络及十个多样化真实社交网络的大规模计算机模拟，我们发现采用这些方法进行信息播种会产生信息鸿沟。研究结果表明，这些算法所选择的影响者未能公平地传播信息，可能加剧社会不平等。为解决这一问题，我们设计了一种多目标算法，旨在同时最大化影响力与信息公平性。实验结果证明，以相对较低的传播效率为代价来降低信息获取脆弱性是可行的。这表明在追求信息传播最大化的过程中，我们无需以牺牲信息平等为代价。