Classification correctness obtained with varying training dataset size.

<p>We evaluated the robustness of our classification algorithms by testing with different sizes for the training and test datasets. The horizontal axis shows the percentage of user accounts used for training, as well as the number of accounts used for training in the 2-Classifier (in blue) and in the 3-Classifier (in red). The remaining accounts were used for testing. Both algorithms perform well above a randomised model in all experiments, even when the training dataset comprised only 30% of the samples (81.2% vs. 52.2% for the 2-Classifier, and 70.8% vs. 32.3% for the 3-Classifier). In these experiments, we used the joint distribution of inter-tweet delay and tweet time as independent variables, and used a total of 86 accounts from each class in the 2-Classifier and 67 accounts from each class in the 3-Classifier. Each experiment was repeated 10 times, and at each time the samples were randomly shuffled among each class.