OPCNN-FAKE: Optimized convolutional neural network for fake news detection
Alsamhi, Saeed H.
MetadataShow full item record
Recently, there is a rapid and wide increase in fake news, de ned as provably incorrect information spread with the goal of fraud. The spread of this type of misinformation is a severe danger to social cohesiveness and well-being since it increases political polarisation and people's distrust of their leaders. Thus, fake news is a phenomenon that is having a signi cant impact on our social lives, particularly in politics. This paper proposes novel approaches based on Machine Learning (ML) and Deep Learning (DL) for the fake news detection system to address this phenomenon. The main aim of this paper is to nd the optimal model that obtains high accuracy performance. Therefore, we propose an optimized Convolutional Neural Network model to detect fake news (OPCNN-FAKE).We compare the performance of the OPCNN-FAKE with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The six regular ML techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) using four fake news benchmark datasets. Grid search and hyperopt optimization techniques have been used to optimize the parameters of MLand DL, respectively. In addition, N-gram and Term Frequency Inverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML, while Glove word embedding has been used to represent features as a feature matrix for DL models. To evaluate the performance of the OPCNN-FAKE, accuracy, precision, recall, F1-measure were applied to validate the results. The results show that OPCNN-FAKE model has achieved the best performance for each dataset compared with other models. Furthermore, the OPCNN-FAKE has a higher performance of cross-validation results and testing results over the other models, which indicates that the OPCNN-FAKE for fake news detection is signi cantly better than the other models.
The following license files are associated with this item: