# 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
For instance, the ensemble I-1 outperformed ensembles I-2 and I-3 for learning rates \(1\times 10^{-5}\) and \(1.5\times 10^{-5}\) when \(T=10\) min. For \(T=30\) min, the ensemble I-3 achieved maximum micro-F1 score for learning rates \(5\times 10^{-6}\) and \(1.5\times 10^{-5}\). For \(T=60\) min, the ensemble I-3 outperformed other ensembles in terms of maximum micro-F1 score for learning rates \(5\times 10^{-6}\) and \(1\times 10^{-5}\). It is this time interval where all ensembles obtained their maximum micro-F1 scores across \(T\) for all chosen time intervals. The overall best micro-F1 score of 62.1% is achieved by the ensemble I-3 for learning rate \(1\times 10^{-5}\). In this time interval, ensembles I-1 and I-3 are better than that of the ensemble I-2. Again, this is due to more diversity of ensemble I-1 and the base learners in ensemble I-3 with LSTM have better representational power than GRU in ensemble I-2. Macro Scores  
From Table 6, for \(T=2,5,10\) and \(30\) min, the macro-F1 scores of the ensemble I-1 are better than that of the ensembles I-2 and I-3 across the chosen learning rates. Again, this is due to the presence of more diversified base learners in ensemble I-1 that helped to surpass other ensembles. It is also noticed that when \(T=10\) and \(30\) min, the performance of the ensemble I-1 drops down across the learning rates compared to \(T=2\) and \(5\) min. This is because for longer time intervals, the lengths of time-series data sequences become shorter thus may overlook small propagation patters presented in the time-series data. For \(T=60\) min, the ensemble I-1 outperformed others in terms of best macro-F1 score for learning rates \(1\times 10^{-5}\) and \(1.5\times 10^{-5}\). When the learning rate is \(5\times 10^{-6}\), the ensemble I-3 surpasses other ensembles. Moreover, in this time interval, for ensembles I-1 and I-2, the results are almost on par with the results that they achieved when \(T=2\) min. In this time interval, the ensemble I-3 achieved its overall best performance across \(T\). The overall best macro-F1 score is obtained by the ensemble I-1 when \(T=2\) min and learning rate of \(1.5\times 10^{-5}\). General Observations  
Furthermore, ensembles I-1, I-2, and I-3 better performed in terms of both micro-F1 and macro-F1 scores when \(T=60\) min over other time intervals w.r.t the chosen learning rates. The only exception is that the ensemble I-1 performed well in terms of macro-F1 score when \(T=2\) min over other time intervals w.r.t the chosen learning rates. In general, both the results are showing us the fact that the performances of ensembles are better when \(T\) is either low (2 min) or high (60 min). This provides a guidance for us to select time interval based on the requirement. For example, if early detection is important, we can pick a low time interval value. In the case of effective prediction, we can go for a higher time interval value. It is also noted that the 10 min time interval caused most of the ensemble implementations, particularly, the ensemble I-1 to achieve low performance in both micro and macro scores. This may be due to the propagation patterns extracted using this time interval value do not have necessary variations, such that it is harder for classification. Another interesting observation is that ensemble I-3 performs poorly with a 5 min time interval in both micro and macro scores. In this case, using a  
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5 min time interval caused high data sparsity, which in turn caused LSTM-based ensemble I-3 to perform poorly.