# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 6 Results and Discussions
### Discussions  
As the PHEME dataset is imbalanced (i.e., non-rumor samples almost double the number of rumor samples), adding more rumor samples to the dataset will help in improving its class balance, and may help classification models to perform better classification. When compared to [33], we noticed that increase in maximum micro and macro averaged F1 scores with addition of two extra events ([PERSON] and [PERSON] Missing events) to the dataset. In case of fixed batch input size, the improvement is 5.7% and 0.4% for micro and macro averaging schemes, respectively. When the learning rate is constant, the improvement is 7.9% for micro averaging scheme. However, the maximum macro F1 score is dropped by 0.7%. Moreover, even though the [PERSON] and [PERSON] missing events are included in the seven events PHEME dataset, only the [PERSON] missing event contributed in adding slightly a greater number of rumor samples to the dataset than [PERSON] event, which is also a supporter of the non-rumor group.  
In addition to this, our data pre-processing method combined with the proposed model helped in improving our previous best score in [33] and achieved 64.3% micro F1 score, which is almost 8% improvement. The performance improvement may seem small, but it is non-trivial to gain huge performances using this dataset, for instance, in [37], extensive feature engineering was conducted for the rumor detection problem on social media using the PHEME dataset with five events. The authors focused on extracting complex features such as content-based and social features, and their best F1 scores are 0.606 and 0.339 for content-based and social features respectively, and when both feature sets are jointly used, the F1 score reached up to 0.607, which is 0.1% improvement. Again, extensive feature engineering needs long time to be completed as some of the features may not be readily available, having complex feature sets challenge hardware resources, which also increases computational complexity that directly impacts training times of classification models. Nevertheless, given the condition that information spreads rapidly on social media, time-taking labor-intensive feature engineering may not be appropriate.  
As our work is solely based on the temporal property (i.e., the tweet creation timestamp value) of tweets we believe using other higher-level features, for example, content-based and context-based features may help in building a more effective classification model. Since our work is intended for the early detection of rumors, this can be used as a primary model and coupled with other models that give more accurate predictions, for building a robust classification model overall. Furthermore, the proposed model's efficiency should be validated with other datasets that are similar to the PHEME dataset. As the PHEME dataset is imbalanced in its nature a balanced dataset may help in improving the efficiency of the proposed model. Finally, the time interval step size can be reduced (i.e., 1 min or half-minute) and/or increased (i.e., 90 min or 2 h) for more deep analysis on the propagation patterns of rumors and non-rumors.