## 5 Experimental Results
### Rumor Detection Results and Comparisons
#### 5.1.1 Results  
We used a 5-fold cross validation technique with the suggested model to validate the findings shown in Table 4. The calculated results are the micro-averaged precision, recall, and F1 scores throughout all five runs for both rumors and non-rumors. The given values for our proposed model are the micro-averaged variance scores across five 5-fold cross-validation repetitions. When the overall performance of SVM, random forest, naive Bayes, and logistic regression were evaluated, it was clear that random forest (RF) exceeded in terms of precision and F1 as shown in Table 4. The foundation of SVM is the support vectors and hyper planes. One of the most often used classification methods is the SVM method. With the baseline classifier, the output of this algorithm is depicted in Figure 6 where it can be seen that SVM performs best in terms with precision 0.41, recall 0.53, and F1 0.44. Figure 6a,c shows the overall performance of proposed outcomes when compared to non-rumors results. Our SVM performs best in terms of precision and recall. The baseline SVM precision results are 0.67, recall is 0.56, and F1 is 0.61. However, our recommended results are 0.69 precision and with 0.63 F1. We performed a 5-fold cross validation and received the same findings as in Figure 6 with the suggested model and the baseline classifiers. The reported values are the micro-averaged precision, recall, and F1 scores for both rumors and non-rumors throughout all five runs. The presented values for our proposed model are the micro-averaged variance scores across five 5-fold cross-validation repetitions. When we compare the overall performance of SVM, RF, NB and LR, it can be seen that in Table 4 logistic regression (LR) outperformed all baseline classifiers in terms of precision and F1, at 0.66 and 0.59, respectively. However, when we compared these four \begin{table}
\begin{tabular}{l l l} \hline \hline
**Sr No.** & **Component** & **Description** \\ \hline
1 & Hardware & Intel Core i5 6 th Gen PC \\
2 & Operating System & Window 10 \\
3 & Memory & 8 GB RAM \\
4 & Server & SQL Server \\
5 & Libraries & Pandas, Sklearn, spaCy, TextBlob \\
6 & Storage & MS Excel \\
7 & Core Programming language & Python \\
8 & IDE & Anaconda Navigator(3) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hardware and software requirements. algorithms with the non-rumor table shown, it could be seen that, overall, naive Bayes had the best performance in terms of precision, and random forest had the best performance in terms of recall and F1 at 0.81, 0.76, and 0.78 shown in Tables 4 and 5, respectively. SVM is built on the foundations of support vectors and hyper planes. The SVM method is one of the most extensively used classification algorithms. The output of this algorithm is shown in Table 2. When comparing the results with the baseline classifier, it can be noted that SVM performs best in terms of precision 0.41, recall 0.53, and F1 0.44. While comparing the results with non rumors, Figure 6 shows both rumor and non-rumor results of the baseline and our proposed model. Our SVM performs best in terms of precision and recall. The baseline SVM precision results are 0.67, recall is 0.56, and F1 is 0.61. However, our recommended results are 0.69 precision; with 0.66, Naive Bayes has the best performance in term of recall. Figure 6 shows the baseline and predicted results in both classes, rumor and non rumor, by using precision, recall and F1. They are the micro-average results of precision, recall and F1 on 50 epochs. We compare the results of random forest with baseline algorithms results in term of rumor and non rumor detection, shown in Tables 4 and 5, respectively. The calculated results are also shown in Figure 7. Our proposed features perform best in terms of precision, recall and F1, at 0.36, 0.756 and 0.41, respectively. While comparing with non-rumors, random forest performs best in term of precision and F1. Figure 6: SVM for rumor and non-rumor detection with baseline and proposed features.