An Emotional Analysis of False Information in Social Media and News Articles
Table 5. The performance of EIN on the clickbaits dataset using 10-fold CV.
['[EMPTY]', 'Accuracy', 'Precision', 'Recall', 'F1']
The results show that our baseline outperforms the proposed clickbait detector with a good margin. Furthermore, the results of the EIN are superior to the LSTM and the Stop_Clickbait detector. Considering emotions in the EIN deep learning approach improved the detection of false information. This is due to the fact that in clickbaits emotions are employed to deceive the reader.