Susceptible user search for defending opinion manipulation

The development of cyberspace offers unprecedentedly convenient access to online communication, thus inducing malicious individuals to subtly manipulate user opinions for benefits. Such malicious manipulations usually target those influential and susceptible users to mislead and control public opinion, posing a bunch of threats to public security. Therefore, an intelligent and efficient searching strategy for targeted users is one prominent and critical approach to defend malicious manipulations. However, the major body of current studies either provide solutions under ideal scenarios or offer inefficient solutions without guaranteed performance. As a result, this work adopts the combination of unsupervised learning and heuristic search to discover susceptible and key users for defense. We first propose a greedy algorithm fully considering the susceptibilities of different users, then adopt unsupervised learning and utilize the community property to design an accelerated algorithm. Moreover, the approximation guarantees of both greedy and community-based algorithms are systematically analyzed for some practical circumstances. Extensive experiments on real-world datasets demonstrate that our algorithms significantly outperform the state-of-the-art algorithm.