Composite User Behaviour Assisted Rumour Detection over Online Social Media

Recently rumour spreading on Online Social Media has become a serious problem which infects the damages to society at both organization and individual levels. Hence, the rumour detection has been emerged as an active research which identifies the rumors automatically. In the rumour detection system, the features have a major role which describes the characteristics of rumour related posts. In this paper, we propose a composite user behaviour related features to describe the characteristics of rumours. Under the composite user concept, we referred the behaviors of both users such as author and reader. Totally, we derive ten features from both users' behavior and fed to machine learning algorithm to train the classifier. Here, two classifiers namely Support Vector Machine and K-Nearest Neighbour are used. For performance evaluation, a standard benchmark dataset is considered and the performance is assessed through precision, recall and F1-score.