The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, this process manifests itself also in online social networks and social media. The key characteristic of successful promotion campaigns is that they take into consideration such opinion-formation dynamics in order to create a overall favorable opinion about a specific information item, such as a person, a product, or an idea. In this paper, we adopt a well-established model for social-opinion dynamics and formalize the campaigndesign problem as the problem of identifying a set of target individuals whose positive opinion about an information item will maximize the overall positive opinion for the item in the social network. We call this problem Campaign. We study the complexity of the Campaign problem, and design algorithms for solving it. Our experiments on real data demonstrate the efficiency and practical utility of our algorithms.
Gionis, Terzi, and Tsaparas REF study from an algorithmic and experimental perspective, the problem of identifying a set of target nodes whose positive opinions about an information item would maximize the overall positive opinion for the item in the network.
Opinion Maximization in Social Networks
Siam International Conference on Data Mining (SDM), 2013
Computer Science Physics