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Algorithms that identify influential people in social networks can help maximize the reach of messages, but a modeling study in PNAS Nexus shows that those same algorithms can disseminate information inequitably, potentially exacerbating existing social inequalities.

From public health campaigns to information about social services, algorithms that identify "influencers" have been used to maximize reach. Vedran Sekara and colleagues used the independent cascade model on synthetic and diverse real-world social networks, including connections between households in multiple villages, connections between political bloggers, Facebook friendships, and scientific collaborations.

The authors found that by maximizing spread, influence maximization algorithms create information gaps, wherein certain outsider groups don't receive important information. Individuals that are likely to be left out are referred to as "vulnerable nodes."

The authors propose a multi-objective algorithm designed to maximize both spread and fairness, which attempts to get information to nodes in the network that are likely to be overlooked by standard methods. The resulting method for choosing which influencers to target results in 6% to 10% fewer vulnerable nodes with a negligible effect on overall reach. According to the authors, using fairer algorithms can help reduce inequity.