Maximizing the Diversity of Exposure in Online Social Networks by Identifying Users with Increased Susceptibility to Persuasion

Individuals may have a range of opinions on controversial topics. However, the ease of making friendships in online social networks tends to create groups of like-minded individuals, who propagate messages that reinforce existing opinions and ignore messages expressing opposite opinions. This creates a situation where there is a decrease in the diversity of messages to which users are exposed (diversity of exposure). This means that users do not easily get the chance to be exposed to messages containing alternative viewpoints; it is even more unlikely that they forward such messages to their friends. Increasing the chance that such messages are propagated implies that an individuals' susceptibility to persuasion is increased, something that may ultimately increase the diversity of messages to which users are exposed. This article formulates a novel problem which aims to identify a small set of users for whom increasing susceptibility to persuasion maximizes the diversity of exposure of all users in the network. We study the properties of this problem and develop a method to find a solution with an approximation guarantee. For this, we first prove that the problem is neither submodular nor supermodular and then we develop submodular bounds for it. These bounds are used in the Sandwich framework to propose a method which approximates the solution using reverse sampling. The proposed method is validated using four real-world datasets. The obtained results demonstrate the superiority of the proposed method compared to baseline approaches.