## 3 Methodology
### Data Collection and Preprocessing  
We have collected the data from the publicly available platform Kaggle.com [53] ([[https://www.kaggle.com/datasets/kumari2000/ukraine-russia-war-twitter-dataset-2022](https://www.kaggle.com/datasets/kumari2000/ukraine-russia-war-twitter-dataset-2022)]([https://www.kaggle.com/datasets/kumari2000/ukraine-russia-war-twitter-dataset-2022](https://www.kaggle.com/datasets/kumari2000/ukraine-russia-war-twitter-dataset-2022)) accessed on 13 December 2022). The data were originally collected through Trackmyhashtag.com utilizing various hashtags such as #ukrainerussiawar, #ukrainewar, #ukraine, and #standwithukraine. They were collected during the middle of the RUW, i.e., August 2022. In total, the data have 50,000 multilingual tweet posts wherein 36,819 tweets are in the English language. We have used only English-language tweets for this work. Among the English language tweets, 36,424 are from non-verified user accounts and 395 belong to verified accounts. The data have the following variables--Tweet Id (identification number for each tweet to uniquely identify), Tweet URL (a URL address to the page where the tweet was posted), Tweet Posted Time (the timestamp when the tweets were posted), Tweet Content (the tweet text posted by users), Tweet Type (whether the tweets are posted as tweet or retweet or reply type), Client (the medium from which the tweets were posted such as iPhone, android, etc.), Refueets Received (number of retweets received by a tweet), Likes Received (the number of likes received by the tweet), Tweet Location (the location from where the tweet was posted; this is generally found empty in data), Tweet Language (this indicates the language of the tweet post such as English, Hindi, Russian), User Id (the id of a twitter user), Name (a screen name/ display name of the Twitter user), Username (actual user name of the Twitter user generally used for logins), User Bio (a short personal description of an account holder), Verified or Non-Verified (whether the account received a verification blue tick badge or not), Profile URL (a URL to go to the user's profile page), User Followers (the number of people who are following a user), User Following (the number of users followed by an account holder), User Account Creation Date (the date when the account was created by a user or age of an account).  
For the data preprocessing: First, we have used only those variables that are in the scope of this study. Thus, we have used tweet content, retweets received, likes received, client, tweet type, user followers, and user following. The remaining variables have been removed (the above mentioned are all variables). Second, we have removed the emojis from the tweet content because our focus is solely on using textual data. Third, we have removed white spaces, and stop words (e.g., you, your, he, she, they, themselves, etc.) because unnecessary spaces and stop words usually will not help ML models to learn any new insights. Moreover, we have removed special characters (', $, *, -, etc. except @ and #). The @ and # are helpful for ML models to learn about user mentions and trending hashtags. Fourth, we have also removed any numerical, character-numerical, and mathematical symbols except punctuation such as exclamation marks (!) and question marks (?) because these marks are helpful to extract emotions. At last, we have performed the lower casing (i.e., making all words of the tweet in lower letters) and padding (i.e., making all sentences of a tweet of equal length by adding extra digits) considering different writing styles and lengths of the tweets.