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A semi-supervised approach to message stance classification
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/tkde.2018.2880192
Georgios Giasemidis , Nikolaos Kaplis , Ioannis Agrafiotis , Jason R. C. Nurse

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages’ stance towards the rumour, a feature known as the “wisdom of the crowd.” Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper, we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-the-art to demonstrate our algorithms’ performance regarding accuracy, speed, and scalability for real-time applications.

中文翻译:

消息立场分类的半监督方法

社交媒体传播正变得越来越普遍;一些有用的,一些虚假的,无论是无意中还是恶意的。越来越多的谣言每天充斥着社交网络。以自主方式确定其真实性是一个非常活跃且具有挑战性的研究领域,提出了多种方法。然而,大多数模型依赖于确定组成信息对谣言的立场,这一特征被称为“群众的智慧”。尽管已经提出了几种有监督的机器学习方法来解决消息立场分类问题,但这些方法存在许多缺点。在本文中,我们认为半监督学习比监督模型更有效,并使用两种基于图的方法来证明它。这不仅在分类准确性方面,而且同样重要,在速度和可扩展性方面。我们使用标签传播和标签传播算法,并对从 Twitter 收集的 72 条谣言和数十万条消息的数据集进行实验。我们将我们在两个可用数据集上的结果与最先进的数据集进行比较,以展示我们的算法在实时应用程序的准确性、速度和可扩展性方面的性能。
更新日期:2020-01-01
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