Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification
Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification
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DOI：
10.18653/v1/2021.findings-acl.341
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
John Dougrez-Lewis;M. Liakata;E. Kochkina;Yulan He
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文献类型：
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作者：
John Dougrez-Lewis;M. Liakata;E. Kochkina;Yulan He
随着社交媒体在过去十年的快速发展，新闻不再由少数几个主流来源控制。用户自己制造了大量可能是虚构的谣言，这就需要自动的准确性分类系统。在这里，我们提出了一种新的方法，根据Twitter上流传的谣言的真实性对其进行自动分类。我们使用一个建立在变分自动编码器上的模型，该模型将推文的信息内容与信息的编写方式分开。这是通过使用立场分类的辅助任务在对抗性学习环境中获得潜在主题向量来实现的。通过这种方式学习的潜在向量被用来预测谣言的真实性，在PHEME数据集上获得最先进的准确度分数。
With the rapid growth of social media in the past decade, the news are no longer controlled by just a few mainstream sources. Users themselves create large numbers of potentially fictitious rumours, necessitating automated veracity classification systems. Here we present a novel approach towards automatically classifying rumours circulating on Twitter with respect to their veracity. We use a model built on Variational Autoencoder which disentangles the informational content of a tweet from the manner in which the information is written. This is achieved by obtaining latent topic vectors in an adversarial learning setting using the auxiliary task of stance classification. The latent vectors learnt in this way are used to predict rumour veracity, obtaining state-of-the-art accuracy scores on the PHEME dataset.1