  Online social networks provide a convenient platform for the spread of
rumors, which could lead to serious aftermaths such as economic losses and
public panic. The classical rumor blocking problem aims to launch a set of
nodes as a positive cascade to compete with misinformation in order to limit
the spread of rumors. However, most of the related researches were based on
one-dimensional diffusion model. In reality, there are more than one feature
associated with an object. The user's impression on this object is determined
not just by one feature but by his/her overall evaluation on all of these
features. Thus, the influence spread of this object can be decomposed into the
spread of multiple features. Based on that, we propose a Multi-Feature
diffusion model (MF-model) in this paper, and a novel problem, Multi-Feature
Rumor Blocking (MFRB), is formulated on a multi-layer network structure
according to this model. To solve MFRB, we design a creative sampling method,
called Multi-Sampling, which can be applied to a multi-layer network structure.
Inspired by martingale analysis, the Revised-IMM algorithm is proposed, and
returns a satisfactory approximate solution to MFRB. Finally, we evaluate our
proposed algorithm by conducting experiments on real datasets, and show the
effectiveness and accuracy of the Revised-IMM algorithm and significantly
outperforms other baseline algorithms.
