# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 6 Results and Discussions
### Experimental Results
#### 6.2.1 Fixed Batch Input Size  
The testing results when the batch input size is fixed are shown in Tables 5 and 6. These testing results are the mean micro and macro averaged F1 scores of all events that are obtained using leave-one-event-out cross-validation along \(T\) by varying learning rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multirow{2}{*}{**Learning Rate**} & \multicolumn{3}{c}{**Macro-F1**} \\ \cline{3-5}  & & **I-1** & **I-2** & **I-3** \\ \hline \multirow{3}{*}{2 min} & \(5\times 10^{-6}\) & 0.44878 & 0.38891 & 0.37119 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.46329 & 0.39504 & 0.38406 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.49849 & 0.38397 & 0.39108 \\ \hline \multirow{3}{*}{5 min} & \(5\times 10^{-6}\) & 0.41544 & 0.35804 & 0.29844 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.42368 & 0.35859 & 0.3125 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.42362 & 0.371 & 0.34597 \\ \hline \multirow{3}{*}{10 min} & \(5\times 10^{-6}\) & 0.34527 & 0.33345 & 0.33153 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.35471 & 0.31419 & 0.31294 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.34021 & 0.32128 & 0.32075 \\ \hline \multirow{3}{*}{30 min} & \(5\times 10^{-6}\) & 0.37669 & 0.32084 & 0.34954 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.38528 & 0.31908 & 0.36389 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.38588 & 0.31528 & 0.38077 \\ \hline \multirow{3}{*}{60 min} & \(5\times 10^{-6}\) & 0.42367 & 0.39506 & 0.45095 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.48757 & 0.39201 & 0.45083 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.48163 & 0.38864 & 0.43825 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean macro averaged F1 testing results of all events that are obtained using leave-one-event-out cross-validation along \(T\) by varying learning rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time Interval**} & \multirow{2}{*}{**Learning Rate**} & \multicolumn{3}{c}{**Micro-F1**} \\ \cline{3-5}  & & **I-1** & **I-2** & **I-3** \\ \hline \multirow{3}{*}{2 min} & \(5\times 10^{-6}\) & 0.53986 & 0.52801 & 0.52835 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.55231 & 0.53131 & 0.53537 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.56656 & 0.53399 & 0.50439 \\ \hline \multirow{3}{*}{5 min} & \(5\times 10^{-6}\) & 0.43673 & 0.43128 & 0.3936 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.43809 & 0.4199 & 0.39489 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.44764 & 0.41844 & 0.40408 \\ \hline \multirow{3}{*}{10 min} & \(5\times 10^{-6}\) & 0.43347 & 0.45515 & 0.46869 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.43594 & 0.4086 & 0.41396 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.42631 & 0.40358 & 0.41814 \\ \hline \multirow{3}{*}{30 min} & \(5\times 10^{-6}\) & 0.55092 & 0.43766 & 0.5524 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.54492 & 0.42296 & 0.53995 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.53428 & 0.45717 & 0.5394 \\ \hline \multirow{3}{*}{60 min} & \(5\times 10^{-6}\) & 0.55717 & 0.58966 & 0.6116 \\ \cline{2-5}  & \(1\times 10^{-5}\) & 0.61769 & 0.56448 & 0.62146 \\ \cline{2-5}  & \(1.5\times 10^{-5}\) & 0.619 & 0.55943 & 0.59565 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean micro averaged F1 testing results of all events that are obtained using leave-one-event-out cross-validation along \(T\) by varying learning rate. Micro Scores  
From Table 5, for \(T=2\) and 5 min, the micro-F1 scores of the ensemble Implementation-1 (I-1) are better than that of the ensemble Implementation-2 (I-2) and Implementation-3 (I-3) across the chosen learning rates. This is due to the fact that it has more ensemble diversity compared with other ensembles, i.e., the presence of base learners designed using Bi-directional RNNs and a model with hybrid architecture that contains a pair of LSTM and GRU layers. In these time intervals, the best scores for the ensemble I-1 are obtained for learning rate \(1.5\times 10^{-5}\). When \(T=10\) min and \(T=30\) min, the micro scores performances are mixed.