# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 5 Experiments and Results
### Comparing ML Models across Feature Extraction Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF–IDF & LR & 0.96 & 0.94 & 0.99 & 0.96 & 1.96 \\ TF–IDF & DT & 0.95 & 0.95 & 0.96 & 0.95 & 2.9 \\ TF–IDF & KNN & 0.88 & 0.86 & 0.94 & 0.89 & 2.25 \\ TF–IDF & GBC & 0.97 & 0.96 & 0.98 & 0.97 & 30.1 \\ TF–IDF & BNB & 0.92 & 0.93 & 0.92 & 0.93 & 1.72 \\ TF–IDF & MNB & 0.89 & 0.84 & 0.98 & 0.89 & 1.71 \\ \hline Empath & LR & 0.66 & 0.64 & 0.87 & 0.74 & 18.98 \\ Empath & DT & 0.77 & 0.79 & 0.78 & 0.79 & 19.1 \\ Empath & KNN & 0.76 & 0.75 & 0.83 & 0.79 & 19.26 \\ Empath & GBC & 0.86 & 0.85 & 0.9 & 0.87 & 22.19 \\ Empath & BNB & 0.62 & 0.65 & 0.65 & 0.65 & 18.96 \\ Empath & MNB & 0.56 & 0.55 & 0.99 & 0.71 & 18.95 \\ \hline Hybrid & LR & 0.98 & 0.97 & 0.99 & 0.98 & 51.71 \\ Hybrid & DT & 0.96 & 0.97 & 0.95 & 0.96 & 51.1 \\ Hybrid & KNN & 0.89 & 0.88 & 0.93 & 0.9 & 52 \\ Hybrid & GBC & 0.99 & 0.99 & 0.98 & 0.99 & 144.82 \\ Hybrid & BNB & 0.92 & 0.94 & 0.91 & 0.93 & 51.7 \\ Hybrid & MNB & 0.9 & 0.87 & 0.98 & 0.92 & 48.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of testing six ML models using different feature extraction methods on Kaggle 2 dataset. BNB and MNB consistently take the least time across all scenarios, possibly due to their simplicity as models based on naive Bayes theory, with lower computational demands. The performance of the models generally declines on the FA-KES dataset, which is smaller in size compared to the first two datasets (see Table 4 for results on the FA-KES dataset). Regardless of the feature extraction method used, all models' accuracy lies between 0.45 to 0.57, significantly lower than those on the first two datasets. This might be due to the smaller dataset size, making it harder for the models to learn sufficient patterns for accurate prediction. That is one major reason to consider these results unreliable [17]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Extract** & **Model** & **ACC** & **Precision** & **Recall** & **F-Score** & **Time** \\ \hline TF-IDF & LR & 0.47 & 0.47 & 0.76 & 0.58 & 0.17 \\ TF-IDF & DT & 0.54 & 0.52 & 0.57 & 0.54 & 0.46 \\ TF-IDF & KNN & 0.49 & 0.48 & 0.7 & 0.57 & 0.19 \\ TF-IDF & GBC & 0.46 & 0.45 & 0.55 & 0.5 & 4.6 \\ TF-IDF & BNB & 0.51 & 0.48 & 0.73 & 0.59 & 0.15 \\ TF-IDF & MNB & 0.49 & 0.48 & 1 & 0.65 & 0.15 \\ \hline Empath & LR & 0.53 & 0.53 & 1 & 0.7 & 5.18 \\ Empath & DT & 0.53 & 0.56 & 0.56 & 0.56 & 5.19 \\ Empath & KNN & 0.51 & 0.54 & 0.58 & 0.55 & 5.35 \\ Empath & GBC & 0.55 & 0.57 & 0.65 & 0.6 & 5.86 \\ Empath & BNB & 0.51 & 0.53 & 0.63 & 0.58 & 5.17 \\ Empath & MNB & 0.53 & 0.53 & 1 & 0.7 & 5.16 \\ \hline Hybrid & LR & 0.52 & 0.57 & 0.6 & 0.59 & 12.41 \\ Hybrid & DT & 0.48 & 0.54 & 0.51 & 0.53 & 12.1 \\ Hybrid & KNN & 0.53 & 0.58 & 0.63 & 0.6 & 12.41 \\ Hybrid & GBC & 0.45 & 0.51 & 0.5 & 0.5 & 22 \\ Hybrid & BNB & 0.54 & 0.56 & 0.93 & 0.7 & 12.39 \\ Hybrid & MNB & 0.57 & 0.57 & 0.97 & 0.72 & 11.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of testing six ML models using different feature extraction methods on FA-KES dataset.