An Emotional Analysis of False Information in Social Media and News Articles
Table 3. Models’ parameters used in the three datasets (News articles, Twitter, Stop_Clickbaits). LSTM: the 3rd baseline, EIN: Emotionally-Infused Network.
['Parameter', 'News Articles LSTM', 'News Articles EIN', 'Twitter LSTM', 'Twitter EIN', 'Stop_Clickbait LSTM', 'Stop_Clickbait EIN']
We have to mention that we use Dropout after the dense layer in the emotional features branch (Dropc) as well as after the attention layer in the other one (Dropd) before the concatenation process. Since it is a multiclass classification process, we use categorical cross-entropy loss function. In this experiment, we consider the third baseline (LSTM) to observe the improvement of the emotional features in the EIN model. Different from the previous experiments, this is a binary classification task. Therefore, we use binary cross-entropy as loss function and we change the Softmax layer to a Sigmoid function.