## 4 Results and Discussion
### Phase 1
Figure 5 shows the word cloud for top buzzwords during the RUW tweet posts. The word cloud is very helpful to gain insights into a specific subject matter such as the RUW with a clearer vision from a different angle. In the word cloud, bigger words indicate a high frequency of appearance that does not mean that small-sized words are not useful. For example, if we look at Figure 5 more closely, we can observe that despite having a war between Ukraine and Russia, countries such as Slovakia, Lithuania, and the USA (see at top of the word cloud in small size) are also a topic of discussion. This finding ties in with the aforementioned Table 1 finding where tweets mentioned the US President's Twitter account (@potus). Overall, the word cloud indicates the high frequencies of Ukraine-related tweets. There is a high mention of Russia's defeat as well (see the word Russia losses). Thus, societal attention is more on Ukraine than on Russia. Figure 6 shows that among the collected tweets, we were able to extract all 27 emotions except the relief emotion which make sense considering the war situations. This indicates the potential of the Emorobetra-based framework to extract various emotions successfully. Despite the societal focus on Ukraine than Russia as shown by the above analysis of the RUW tweet content, surprisingly, approx. 81% of tweets' stance is neutral and 3% of tweets indicate approval emotions with 3% of optimism. Moreover, 2.5% of tweets indicate the curiosity emotion with approximately 1.5% of gratitude, 1.5% of admiration, and 1% each of caring, joy, and confusion. Rest emotions such as amusement, anger, annoyance, desire, disappointment, disapproval, disgust, fear, embarrassment, excitement, grief, love, nervousness, pridge, realization, remorse, sadness, and surprise, covers 4.5% of tweets. Here, we can sense that the data collection period may have played an important role in this result because the data were collected during the mid-war (in Aug 2022) when people may have had more clarity of the conflict reasons. Moreover, large populated countries  
Figure 4: Distribution of tweets according to tweet type:original tweet, retweet, and reply tweet. Figure 5: Word cloud of most buzzed words among the collected RUW tweets. like India which has high numbers of Twitter users and showed a neutral instance for the war [74; 75; 76], may have to lead the Twitter conversations. For a clearer view and to showcase the potential of our Emoroberta-based framework's competency we have shown the example tweets with their associated emotions in Table 2. By looking and reading at the tweet in Table 2 one can observe the accurately labeled emotions. We have used an asterisk sign (i.e., **) wherever necessary to hide abusive context. Moreover, these tweets are already present on Twitter in real-time. We did not perform any spelling corrections because this will ruin the originality of the tweet posts. Therefore, in Table 2 we can observe the usage of misspelled words (e.g., ruzzia instead Russia) as a metaphor or to spare oneself from any social content-related issues such as banning or suspension. However, such misspelled words can portray the meaning of the word and its context. \begin{table}
\begin{tabular}{c c} \hline \hline
**Emotion** & **Tweet** \\ \hline Admiration & “Great to see @[PERSON] giving more financial support to Ukraine. Probably his last act in his office. \#UkraineWar” \\ \hline Amusement & “Hahahahaha, the Ukrainians did win allready with humor, now kick some Russian behinds! \#Crimea \#kerch \#bridge \#Ukraine” \\ \hline Anger & “\#Russia strike on \#Kharkiv in northeast \#Ukraine kills at least 7 people and wounded 16 others.