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

A Gradient Tree Boosting based Approach to Rumor Detecting on Sina Weibo