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

• URL: http://arxiv.org/abs/2405.12764v2
• Date: Wed, 30 Oct 2024 10:42:50 GMT
• Title: Detecting and Mitigating Bias in Algorithms Used to Disseminate Information in Social Networks
• Authors: Vedran Sekara, Ivan Dotu, Manuel Cebrian, Esteban Moro, Manuel Garcia-Herranz,
• Abstract summary: Influence algorithms are used to identify sets of influencers. We show that seeding information using these methods creates information gaps. We devise a multi-objective algorithm which maximizes influence and information equity.
• Score: 0.03883607294385062
• License:
• 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.

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