## 5 Experimental Results
### Rumor Detection Results and Comparisons
#### 5.1.1 Results
Now, we compare the results of naive Bayes with the baseline classifier in term of rumors that are shown in Table 4. Our proposed features perform best using naive Bayes in terms of precision recall and F1 that are 0.42, 0.83 and 0.55, respectively, as shown in Figure 8. However, when we compare the results of naive Bayes with the non-rumor results that are shown in Table 5, our proposed features perform best when we compare the state-of-the-art baseline features in term of precision, recall and F1. Similarly, logistic regression outperforms both classes of rumors and non-rumors as shown in Tables 4 and 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Baseline Rumors Result** & \multicolumn{4}{c}{**Proposed Rumor Result**} \\ \hline  & **Precision** & **Recall** & **F-1** & **Precision** & **Recall** & **F-1** \\ \hline SVM & 0.35\(\pm\)0.45 & 0.45 \(\pm\) 0.18 & 0.39 \(\pm\) 0.84 & 0.41 \(\pm\) 0.74 & 0.53 \(\pm\) 0.39 & 0.44 \(\pm\) 0.55 \\ Random Forest & 0.32 \(\pm\) 0.72 & 0.10 \(\pm\) 0.83 & 0.15 \(\pm\) 0.71 & 0.36 \(\pm\) 0.38 & 0.56 \(\pm\) 0.91 & 0.41 \(\pm\) 0.12 \\ Naïve Bayes & 0.40 \(\pm\) 0.89 & 0.76 \(\pm\) 0.19 & 0.52 \(\pm\) 0.67 & 0.42 \(\pm\) 0.38 & 0.83 \(\pm\) 0.74 & 0.55 \(\pm\) 0.62 \\ Logistic Regression & 0.36 \(\pm\) 0.12 & 0.47 \(\pm\) 0.92 & 0.41 \(\pm\) 0.61 & 0.66 \(\pm\) 0.34 & 0.54 \(\pm\) 0.27 & 0.59 \(\pm\) 0.24 \\
**Bi-LSTM-RNN** & 0.61 \(\pm\) 0.64 & 0.67 \(\pm\) 0.39 & 0.63 \(\pm\) 0.83 & 0.76 \(\pm\) 0.14 & 0.73 \(\pm\) 0.74 & 0.74 \(\pm\) 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Rumor Class Result with state of the art baseline. Figure 7: RF for rumor and non-rumor detection with baseline and proposed features. When we compare the results of logistic regression with the state-of-the-art baseline classifier in term of rumors and non rumors, Figure 9 shows that our proposed features with the baseline model perform best in overall precision, recall and F1, while, when comparing with the non-rumor class, logistic regression also performs best in precision, recall and F1, which are 0.70, 0.70, and 0.74, respectively. Now, we apply our proposed deep learning model on the same dataset. It produces best results overall for both classes of rumor and non rumor, compared with the state-of-the-art baseline classifier in terms of precision, recall and F1 as shown in Figure 10. These computed results shows that our model and new proposed features perform best when comparing all baseline classifiers, in detecting rumors and non-rumors by using only the text data of a tweet without content and social-based features as shown also in Tables 4 and 5. Bi-LSTM is the most widely used categorization approach. In this algorithm, we apply deep learning mode on text data. The results of this approach are shown in Figure 10. When compared to the baseline classifier, our suggested Bi-LSTM RNN has a precision of 0.77, a recall of 0.73, and an F1 of 0.74. When comparing the non-rumor findings to the baseline classifier, as seen in Table 3, Bi-LSTM-RNN performs best in terms of precision and recall. In the baseline LSTM RNN, precision is 0.82, recall is 0.77, and F1 is 0.79; however we recommend 0.87 precision, 0.88 recall, and 0.87 F1 score. Table 6 shows the average precision score of different classifiers using only content-based features and after using hybrid content and social-based features to measure the impact of social-based features. On the Ferguson dataset, when applying naive Bayes on only content-based features, the average precision score is 0.64, and after this, both social- and content-based features were merged and naive Bayes was applied, increasing the result by 0.2. In some classifiers, they have much importance for social-based features, and some classifiers have no positive impact on social-based features.