Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?
TABLE II: Performance of the methods on the 11Headlines2Media Corpus[] dataset
['Method Without Pre-trained Vectors', 'Method *Chakroborty et al.\xa0', 'Precision 0.95', 'Recall 0.90', 'F-measure 0.93', 'Accuracy 0.93', 'Cohen’s  [ITALIC] κ', 'ROC-AUC 0.97']
We use the \IfEqCase11Headlines2Media Corpus[] dataset to evaluate our classification model. We perform 10-fold cross-validation to evaluate various methods with respect to accuracy, precision, recall, f-measure, area under the ROC curve (ROC-AUC) and Cohen’s κ. To avoid randomness effect, we perform each experiment 5 times and present the average. There are in total seven methods. We categorize them based on the use of pre-trained vectors. Note that we report performances of Chakroborty et al. and Anand et al. We keep Anand et al. with the methods which use pre-trained vectors. Each word embedding has 300 dimensions. Moreover, the training and test sets of the earlier dataset are not available. So, we could not compare our methods with them using the same test bed.