TopicRRC: a Reverse Sampling Algorithm for Maximizing Online Topic-Aware Rumor Containment

In the digital age, the rapid spread of misinformation and rumors poses a critical challenge for social media platforms and users. Existing rumor containment methods often overlook the diverse range of topics associated with information and fail to consider user interests, resulting in incomplete understanding of rumor propagation. To address this issue, we introduce the Topic-aware Rumor-Truth Cascade (TRTC) model, which incorporates user interests and topic relevance to better capture the dynamics of information propagation. We define the Topic-aware Rumor Containment Maximization (TRCM) problem within TRTC model and prove its monotonicity and submodularity properties. To solve this problem, we propose Topic-aware Reverse Reachable Count (TopicRRC), an efficient index-based algorithm that leverages reverse sampling techniques to quickly identify effective truth seed sets for multiple online TRCM queries, thereby reducing both computational time and memory usage. The extensive experiments on real-world datasets demonstrate that TopicRRC outperforms existing approaches in terms of rumor containment effectiveness and computational efficiency. © 2002-2012 IEEE.