论文标题
合奏深度学习时间序列表示社交媒体中谣言检测的推文
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
论文作者
论文摘要
社交媒体是及时信息共享的流行平台。对于Twitter等社交媒体平台的重要挑战之一是，如果没有系统的新闻验证过程，是否信任新闻。另一方面，鉴于快节奏的社交媒体环境，及时发现谣言是一项非凡的任务。在这项工作中，我们提出了一个集成模型，该模型使用Twitter数据的时间序列矢量表示，对深度神经网络的预测进行了多数投票，以及时检测谣言。通过将提出的数据预处理方法与集成模型相结合，在使用Pheme数据集的实验中已经证明了谣言检测的更好性能。实验结果表明，与基准相比，与微F1得分相比，分类性能已提高了7.9％。
Social media is a popular platform for timely information sharing. One of the important challenges for social media platforms like Twitter is whether to trust news shared on them when there is no systematic news verification process. On the other hand, timely detection of rumors is a non-trivial task, given the fast-paced social media environment. In this work, we proposed an ensemble model, which performs majority-voting on a collection of predictions by deep neural networks using time-series vector representation of Twitter data for timely detection of rumors. By combining the proposed data pre-processing method with the ensemble model, better performance of rumor detection has been demonstrated in the experiments using PHEME dataset. Experimental results show that the classification performance has been improved by 7.9% in terms of micro F1 score compared to the baselines.
