## 5 Discussion  
First, we discuss the results of the comparison between the method using emotional expressions and the method using word expressions.  
The sentiment dictionary model exhibits significant accuracy degradation in some periods, such as in Period 1, while Period 3 is the only period in which it does not suffer significant accuracy degradation. The small degradation of accuracy in Period 2 indicates that the emotional dictionary model is more resistant to accuracy degradation than the coupled and BERT models, which use word representations, except for in some periods.  
Next, we discuss the sudden drift that occurred in Periods 1 and 3 in terms of the social context of those periods.  
First, in Period 1, the classification results using the coupled, sentiment dictionary, and BERT models reveal that the number of cases in which fake news was determined to be real news increased. This period coincided with a time when terrorist activities by Islamic extremist groups were a critical topic [32]. For such a controversial topic, neither emotional nor word-based methods could detect the news because the inciting words usually necessary for the news to spread widely were not used.  
Next, in Period 3, the classification results of the coupled and BERT models revealed an increase in the number of cases in which real news was determined as fake news. In this period, the hurricanes in the Atlantic Ocean caused considerable damage to the United States. The following cases are related to this topic [33].  
* Hurricane Florence: Power outages top 700,000, could reach 3 million.  
Figure 8: F-measures of the coupled, sentiment, and word models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**word** & **emo** & **word+emo** \\ \hline
0.373 & 0.312 & 0.377 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average of the F-values of the coupled, sentiment, and word models.  
* Hurricane Florence nears Carolinas as 1 million-plus ordered to Evacuate.  
In the case of news on such a large-scale disaster, even if the news were true, the news was determined to be false if we focused on word expressions, resulting in a loss of accuracy. When we focused only on emotional expressions, no inciting features were observed, and the classification could be performed as before without any accuracy degradation.  
Next, we discuss the results of the comparison with [PERSON] et al.'s model. The emotion model could not prevent performance degradation probably because the features of the words were more pronounced. As emotional GloVe is an embedding method that extends the learned word vector space, it contains both word features and emotion features. Therefore, no significant difference was observed between the accuracy of the emotion and word models.  
The transition in the accuracy of the emotion dictionary model confirmed the resistance of emotion-based methods for accuracy degradation, but the classification accuracy was low. An emotion dictionary with a larger vocabulary and detailed emotion features can improve the performance of emotion-based methods. Further study on the methods combining words and emotion features is necessary. In the future, models and learning methods that can achieve both accuracy and tolerance for accuracy degradation should be devised to reduce the cost required for re-training.