谣言
计算机科学
社会化媒体
微博
社交网络(社会语言学)
代表(政治)
开放的体验
机制(生物学)
人工神经网络
人工智能
机器学习
数据科学
万维网
心理学
哲学
认识论
法学
政治
社会心理学
公共关系
政治学
作者
Han Guo,Juan Cao,Yazi Zhang,Jinlong Guo,Jintao Li
标识
DOI:10.1145/3269206.3271709
摘要
Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.
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