  Rumor detecting on microblogging platforms such as Sina Weibo is a crucial
issue. Most existing rumor detecting algorithms require a lot of propagation
data for model training, thus they do not have good detecting accuracy at the
early stage after a rumor message is posted. In this paper, we propose to use
gradient tree boosting (GTB) approach to rumor detecting, based on which a
rumor detecting algorithm is developed. At the same time, the GTB-based
approach makes it easy to conduct feature selection, and a feature selection
algorithm is developed. Experiments on a widely used dataset of Sina Weibo show
that the proposed detecting algorithm outperforms the state-of-the-art
detecting algorithms; moreover, it has the highest detecting accuracy at the
early stage. This work seems to be the first to use GTB-based approach in rumor
detecting, and the results suggest that it may be a promising one.
