## 7 Results  
### Fake News Detection Performance  
User behavior clues are important for detecting fake news [19]. Fake news detection has previously been achieved using word embeddings and bag-of-words (see Section 2 for more details). However, neither of these representations can capture contextual information. As opposed to that, our system uses highly sophisticated representations based on bidirectional encoder representations from transformers (BERT) [29]. By training a transformer bidirectionally, BERT can learn contextual relationships among words. In addition, BERT reads the entire word sequence at once, in contrast to earlier systems that either looked at a text sequence from left to right or combined left-to-right and right-to-left training. This trait enables the model to understand a word's context based on all of its surroundings. The tables below present the results of the proposed models and the official baseline models in the two datasets. The best performance scores are highlighted in bold. Modeling user posting behavior information in addition to news text was effective for detecting fake news. The test results in the PolitiFact dataset shown in Table 2 demonstrate that the highest-performing model is BERT-CNN-BiGRU-ATT, which outperformed the other official baselines. The BERT\({}_{base}\)-BiGRU-CNN-ATT model obtained 87.05% accuracy, 86.90% for precision, 91.25% for recall, and 89.02% for the F1 score. The BERT\({}_{base}\)-CNN-BiGRU model obtained a performance of 93.59% for precision, 91.37% for accuracy, 91.25% for recall, and 92.41% for the F1 score. Summaries of the prediction results of each model in the PolitiFact dataset are shown in the forms of confusion matrices in Figures 4-6. Regarding PolitiFact, all models showed similar confusion matrix results, with BERT\({}_{base}\)-CNN-BiGRU-ATT yielding the best results. Figure 7 shows the overall scores across all of the models. The comparison of results of the GossipCop dataset (using the combination of user-behavior cues with news text) is described in Table 3 and shows that the BERT\({}_{base}\)-BiGRU-CNN-ATT model outperformed the existing baselines and other counterpart models. The test results show that the BERT\({}_{base}\)-BiGRU-CNN-ATT model provides the best performance with accuracy and an F1 score of 90.34% with 93.79% for precision. The BERT\({}_{base}\)-CNN-BiGRU-ATT model obtains a performance of 88.49% for accuracy, 91.07% for precision, 94.14% for recall, and 92.58% for the F1 score. The last proposed BERT\({}_{base}\)-CNN-BiGRU model yields an accuracy of 90.10%, precision of 90.73%, recall of 96.91%, and an F1 score of 93.72%. Summaries of the prediction results of the models in the GossipCop dataset are shown in the forms of confusion matrices in Figures 8-11, showing the overall scores across all of the models. We were also eager to further assess the effectiveness of the proposed architectures. Therefore, we conducted a case study where we tested the model's performance by ignoring user posting behavior features and comparing the model's performance. We observed that when neglecting user posting behavior information, the performance deteriorated in the two datasets. The experimental results are tabulated in Tables 4 and 5 for the PolitiFact and GossipCop datasets, respectively. The best-performing model is shown to be BERT\({}_{base}\)-CNN-BiGRU in the two datasets. The BERT\({}_{base}\)-CNN-BiGRU-ATT model obtained a performance of 87.77% for accuracy, 86.21% for precision, 93.75% for recall, and 89.82% for the F1 score in the PolitiFact dataset, lower than the results obtained by existing studies [36; 37], while the model achieved an accuracy of 85.84%, precision of 88.49%, recall of 93.16%, and 90.98% for the F1 score in the GossipCop dataset. The BERT\({}_{base}\)-BiGRU-CNN-ATT model yielded an accuracy of 88.49%, and precision, recall, and F1 score of 86.44%, which is on par with [36; 37] in the Politifact dataset and higher than the official baselines in the GossipCop dataset.