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A Topic Aware-based Approach to Maximize Social Influence

The use of social networks has shown great potential for information diffusion and formation of public opinion. One key problem that has attracted researchers interest is Topic-based Influence Maximization, that refers to finding a small set of users on a social network that have the ability to influence a substantial portion of users on a given topic. The proposed solutions, however, are not suitable for large-scale social networks and must incorporate mechanisms for determining social influence among users on each topic of interest. Consequently, for these approaches, it becomes difficult or even unfeasible to deal quickly and efficiently with constant changes in the structure of social networks. This problem is particularly relevant as the topics of interest of users and the social influence they exert on each other for every topic are considered together. In this work, it is proposed a scalable solution, that makes use of data mining over an information propagation log, in order to directly select the initial set of influential users on a particular topic without the need to incorporate a previous step for learning users social influence with regard to that topic. As an additional benefit, the targeted seed set also offers an approximation guarantee of the optimal solution. Finally, it is presented a design of experiments over a data set containing information propagation data from a real social network. As main results, we have found some evidences that the proposed solution maintains a trade-off between scalability and accuracy.