It is a fact that most of the rumours related to hot events or emergencies can be propagated rapidly on the hotbed of online social networks. In order to track the standpoints of the participants of rumour topics to regulate the development of rumour, we propose a multi-features model combining classifiers to classify the rumour standpoints, defined as classifying the standpoints of online social network conversations into one of ‘agree’, ‘disagree’, ‘comment’ or ‘query’ on previous comment about the rumour. Testing the performance of the combinatorial model – decision tree with adaptive boosting classifier and extremely randomised trees with adaptive boosting classifier – on different features, that is, structuring the weight matrix based on combination of term frequency (TF), inverse document frequency (IDF) and term frequency – inverse document frequency (TFIDF) method and constructing the features vector with Word2vec method. The experiments show that the combinatorial classifiers that exploit different combination features in the online social network conversations outperform binary classification; especially, the topology of the social network has a highly positive impact on the classification results. Furthermore, the ‘comment’ and ‘query’ of rumour standpoints have a better classification effect based on the features of different categories.

中文翻译：

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基于组合分类器的在线社交网络谣言立场分类

事实上，大多数与热点事件或突发事件相关的谣言都可以在在线社交网络的温床中迅速传播。为了跟踪谣言话题参与者的立场以规范谣言的发展，我们提出了一种结合分类器的多特征模型对谣言立场进行分类，定义为将在线社交网络对话的立场归类为“同意”之一。 , 'disagree', 'comment' 或 'query' 对之前关于谣言的评论。测试组合模型的性能——带有自适应提升分类器的决策树和带有自适应提升分类器的极端随机树——在不同特征上的性能，即基于词频 (TF) 的组合构造权重矩阵，逆文档频率 (IDF) 和词频 – 逆文档频率 (TFIDF) 方法并使用 Word2vec 方法构建特征向量。实验表明，利用在线社交网络对话中不同组合特征的组合分类器优于二元分类；尤其是，社交网络的拓扑结构对分类结果有非常积极的影响。此外，谣言立场的“评论”和“查询”基于不同类别的特征具有更好的分类效果。社交网络的拓扑结构对分类结果有非常积极的影响。此外，谣言立场的“评论”和“查询”基于不同类别的特征具有更好的分类效果。社交网络的拓扑结构对分类结果有非常积极的影响。此外，谣言立场的“评论”和“查询”基于不同类别的特征具有更好的分类效果。