基于梯度树提升的新浪微博谣言检测方法
A Gradient Tree Boosting based Approach to Rumor Detecting on Sina Weibo
摘要：
在新浪微博等微博平台上进行谣言检测是一个至关重要的问题。现有的大多数谣言检测算法需要大量的传播数据进行模型训练，因此在谣言消息发布后的早期阶段没有很好的检测准确性。本文提出将梯度树提升（GTB）方法用于谣言检测，并在此基础上提出了一种谣言检测算法。同时，基于GTB的方法使特征选择的进行变得容易，并提出了一种特征选择算法。在新浪微博数据集上的实验结果表明，该检测算法的检测性能优于现有的检测算法，并且在早期阶段具有最高的检测准确率。本文首次将基于GTB的方法应用于谣言检测中，实验结果表明，该方法具有良好的应用前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
