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首页> 外文期刊>Knowledge and information systems >Empower rumor events detection from Chinese microblogs with multi-type individual information
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Empower rumor events detection from Chinese microblogs with multi-type individual information

机译：Empower Rumor事件检测来自中国微博的多型个人信息

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摘要

Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness of a rumor event, it will be verified and authenticated with multi-type individual information, especially with individuals' emotional expressions to events and their own credibility. This paper presents a novel two-layer GRU model for rumor events detection based on multi-type individual information (MII) and a dynamic time-series (DTS) algorithm, named as MII-DTS-GRU. Specifically, MII refers to adopt the sentiment dictionary to identify fine-grained human emotional expressions to events and fuse with the individual credibility. Besides, the DTS algorithm retains the time distribution of social events. Experimental results on Sina Weibo datasets show that our model achieves a high accuracy of 96.3% and demonstrate that our proposed MII-DTS-GRU model outperforms the state-of-the-art models on rumor events detection.
机译：在线社交媒体已成为传播谣言事件的理想场所，其伴随着沟通和信息传播，这会自动提出戴博谣言事件的困难。为应对这种挑战，传统的分类方法依赖手动标记的功能必须面对令人生畏的人类努力。考虑到谣言事件的真实性，它将通过多型个性信息进行验证和认证，特别是个人对事件的情感表达及其自身信誉。本文提出了一种基于多型单独信息（MII）和动态时间序列（DTS）算法的谣言事件检测的新型两层GRU模型，名为MII-DTS-GRU。具体而言，MII是指采用情感词典，以将细粒度的人类情感表达识别对事件和融合的融合和融合。此外，DTS算法保留了社交事件的时间分布。新浪微博数据集的实验结果表明，我们的模型实现了96.3％的高精度，并证明了我们所提出的MII-DTS-GRU模型优于谣言事件检测的最先进模型。

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