# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 5 Experiments and Results
### Results and Discussion on Incremental Learning
#### 5.4.3 Pre-Training on Trimmed-Scraped for Testing on Scraped  
In Tables 16 and 17, we present the results obtained with and without incremental learning. Notably, the pre-trained model required 0.008 s for training in the case of BNB and 0.003 s for MNB. The experimentation conducted with the Scraped dataset yielded mixed outcomes upon the implementation of incremental learning. While there was a notable improvement in runtime for both the BNB and MNB models, the performance metrics displayed significant fluctuations. To elaborate, focusing on the Bernoulli naive Bayes (BNB) model, there was an enhancement in accuracy, rising from 0.77 to 0.82, alongside a reduction in runtime from 0.035 to 0.0114. Despite these relatively minor adjustments, they imply that incremental learning may offer advantages for this specific model. Conversely, the multinomial naive Bayes (MNB) model experienced a substantial decrease in performance, with accuracy plummeting from 0.76 to 0.58. This emphasizes that the benefits of incremental learning may not be consistent across all models or datasets. The discrepancy in performance is further underscored by the MNB model's high precision but low recall score post incremental learning. This suggests that while the model correctly identifies positive instances (high precision), it fails to identify all positive instances (low recall), particularly regarding the classification of fake news. This inclination toward conservative labeling, where news articles are labeled as false only when the model is highly certain, may contribute to the observed decrease in accuracy. One plausible explanation for these findings is rooted in the essence of incremental learning. This approach operates under the assumption that the model receives regular updates with new data. However, if the incoming data deviates from the overall distribution of the dataset or introduces novel concepts unfamiliar to the model, classification accuracy may suffer. This observation aligns with the Scraped dataset's characteristics, comprising news articles sourced from diverse web sources through web crawlers. Given this diversity, it is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & BNB & 0.77 & 0.75 & 0.58 & 0.65 & 0.035 \\ TF-IDF & MNB & 0.76 & 0.98 & 0.34 & 0.51 & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Results of Scraped dataset with incremental learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & BNB & 0.9 & 0.89 & 0.9 & 0.89 & 0.14 \\ TF-IDF & MNB & 0.86 & 0.9 & 0.82 & 0.85 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of WELFake dataset without incremental learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & BNB & 0.82 & 0.8 & 0.67 & 0.73 & 0.0204 \\ TF-IDF & MNB & 0.58 & 0.95 & 0.42 & 0.58 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Results of Scraped dataset with incremental learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & BNB & 0.9 & 0.89 & 0.9 & 0.89 & 0.14 \\ TF-IDF & MNB & 0.86 & 0.9 & 0.82 & 0.85 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Results of WELFake dataset with incremental learning. conceivable that the incoming data may not align closely with the distribution or features of the initial data used for model pre-training. The overall analysis of the experiments with incremental learning highlights its potential to enhance computational efficiency, evidenced by reduced runtimes across all cases. However, its impact on model performance varied depending on the dataset and the model under consideration.