The classification of rumour standpoints in online social network based on combinatorial classifiers
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.