## 4 Results and Discussion
### Phase 1  
During phase one of our proposed approach, we extracted the emotions to further label the RUW tweets for supplying to phase 2 for the classification task. Following Table 1 shows the distinctive analysis based on the collected dataset. We have found that war and Ukraine-related hashtags are trending more than Russia-related hashtags. #fake is also trending (see Table 1), which indicates the probability of fake or misinformation proliferations on Twitter related to war which is a sign of an information war between Russia and Ukraine. During the analysis of user mentions (denoted by @ on Twitter posts), we found that tweets were mentioning political leaders such as Ukrainian president [PERSON] (@zelenskyyua) and the US president's office (@potus). This indicates the societal attention on western nations' ties with Ukraine. However, there can be many reasons to lead the mention of political agencies such as replying to any specific tweet, grabbing the attention of politicians, etc. Furthermore, the Twitter website followed by android and iPhone is a major communication medium for posting RUW-related tweets. However, there is a presence of social bot tweets as well that needs to be observed for any misconduct on Twitter such as spreading fake news, abusive content, or provocative posts. To analyze the societal engagement of \begin{table}
\begin{tabular}{c c} \hline \hline
**Analysis** & **Values** \\ \hline \multirow{4}{*}{Top Hashtags} & \#dugina \#Ukrainewar \#FSB \#fake, \\  & \#russianarmy, \#ukraine, \#nafo, \#fellas, \\  & \#ukrainerussiawar, \#ukraineunderattack, \\  & \#standwithukraine, \#supportokraine \\ \hline \multirow{4}{*}{Top Mentions} & @isssandjumal, @zelenskyyu, @youtube, \\  & @potus, @rybar, @tovaobrien, @himarstime, \\  & @todayfm\_nz \\ \hline \multirow{4}{*}{Used mediums} & 41\% twitter website users, 32\% android users, \\  & and 19\% iPhone users \\  & 8\% are from other sources such as iPad, \\  & zapiercom, TweetDeck, and bots. \\ \hline \multirow{4}{*}{Tweet Likes} & Total likes—89,287, Average like per \\  & tweet—approx. 2, Max likes—16,987, \\  & and Min likes—0 \\ \hline \multirow{4}{*}{Retweets} & Total retweets—20,951, Average \\  & retweets—approx. 0.6, Max retweets—5764, \\  & and Min retweets—0 \\ \hline \multirow{4}{*}{Followers} & Total followers—152,464,964, Average follower \\  & per tweet—4141, Max followers—14,515,730, \\  & and Min Followers—0 \\ \hline \multirow{4}{*}{Followings} & Total user following—60,554,956, Average per \\  & tweet following—1644, Max \\ \cline{1-1}  & following—1,434,649, and Min following—0. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distinct analysis of collected RUW tweets. RUW-related posts, we have observed that approximately on average each tweet received 2 likes with a maximum of 16,987 likes and 5764 retweets. The data have user tweets that have a maximum of 14,515,730 followers and a user account that is following a maximum of 1,434,649 other users. This indicates the tweet's reach among Twitter audiences. Figure 2 shows the most liked and retweeted posts in the collected data, where the tweet content highlights the spread of misinformation related to military personnel. This finding ties in with the Table 1 hashtag analysis (see #fake). Figure 3 indicates the tweet lengths that show a maximum of tweets are under the 100 letters limit and as a maximum a few tweets have been posted with the full 280 letters limit of Twitter. However, we have removed the URLs and stop words that may have shrilled the tweet length. Figure 4 indicates the distribution of the tweet types among the collected data. Approximately 67% of tweets belong to the retweet category (a tweet that is posted multiple times by many people) whereas original tweets and replies to original tweets are 26% and 7% respectively. This indicates high societal participation to proliferate RUW-related tweets. Figure 3: Distribution of the tweet length—number of characters in the tweets. Figure 2: The most liked and retweeted post on Twitter among all collected tweets.