Approximating the k-Minimum Distance Rumor Source Detection in Online Social Networks

Abstract

Online Social networks (OSNs) are now one of the main resources for people to keep abreast of current news and to exchange opinions about new products and social trends, etc. However, unethical use of OSNs also provides a convenient conduit to the diffusion of malicious rumors and misinformation, thus it is of significant importance to discover rumor diffusion and detect the rumor source. This is a very challenging task, as shown in many existing works, e.g., even in the regular tree graphs, the accuracy of detecting the information source from a diffusion snapshot cannot exceed 31%. To overcome this issue, in this work, we propose a novel system framework for information source detection in OSNs and investigate a new rumor source detection problem, called $k$-Minimum Distance Rumor Source Detection (k-MDRSD). Specifically, given a rumor spreading snapshot, our target is to find a small set of rumor candidates which can be used as initial seeds for further iterative query or investigation. To this end, we introduce a notion, called distance error, for rumor candidate sets and formulate the k-MDRSD problem. Resorting to methods from Combinatorics, we develop a near optimal algorithm for k-MDRSD. By experimental simulation, we show that the proposed k-MDRSD significantly improves the likelihood of detecting rumor sources or trend-setters in OSNs.