Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Table 2

Experimental setup, features, and description.

Experiment-setup	Features	No. of features	Experimental description
Experiment 1	Contain only single attribute “statement”	1	Dataset 1 contains only single feature “statement” and provides the embedding vector of dataset 1 as input to Bi-LSTM-GRU-dense deep learning model, and results were recorded
Experiment 2	Numeric and categorical features excluding statement	9	The dataset 2 included only categorial or numeric data. For this dataset, the first model, i.e., deep learning dense model was used, and results were collected
Experiment 3	Contain all features including “statement”	10	For the dataset 3, the ensemble technique of the proposed model was applied, i.e., for the “statement” feature, the Bi-LSTM-GRU-dense deep learning model was used, while for the rest of 9 features, the deep learning dense model was used. The result of each model is then ensembled by using ensemble voting techniques and is recorded