Temporal Rumor Blocking in Online Social Networks: A Sampling-Based Approach

Abstract

Online social networks (OSNs) are one of the most popular services available on the Internet. They are also considered as the primary source of news for many people. In spite of all the advantages of OSNs, dissemination of rumors has become a major concern. Influence blocking maximization (IBM) is the problem of determining savior users to limit the spread of misinformation as much as possible. Although the IBM problem has been widely studied, most of existing works neglect the effect of deadline and delay. In this article, we study Temporal IBM and propose a sampling-based approach (TIBM-M) with both theoretical guarantee and practical runtime efficiency. We perform extensive experiments on four real-world datasets and both experimentally and theoretically show that TIBM-M consistently matches the greedy algorithm in terms of effectiveness while significantly improves the efficiency. The results also indicate that TIBM-M has better blocking effect than other methods in all conditions.