The goal of this research is to understand how Americans react to Russian war against Ukraine through their own tweets or comments on somebody's tweets related to such topic. Originally, I scraped (i.e. extracted) tweets based on specific keywords, language (English and Russian) and two specific and very significant date ranges: from February 24 until February 26 that marked Hague issued warrant for Putin’s arrest. As I mentioned in my write out on published Tableau visualization, I used the following most common keywords (i.e. "Russian war in Ukraine", "Russian propaganda", "Support Ukraine"), then I analyzed each of generated files based on pattern/words that are common for many tweets from those files and noticed that a few patterns like ["Ukraine", "nazi"], ["Trump", "Ukraine"] make up majority of tweets that have been extracted based on keyword "Support Ukraine". That narrative about “nazi” in Ukraine is mostly parroting statements expressed by former Fox News host Tucker Carlson who, in his turn, took such narrative from Russian propagandists’ mouths. So, I decided to add another date for analysis which has starting point right after Tucker Karlson was fired - just to see if number of tweets that used narrative about “nazi” in Ukraine are reduced significantly. I used NLP technique (specifically LDA (Latent Dirichlet Allocation) model) to analyze entire file generated for keyword “Support Ukraine" as well as files generated for each of the following patterns: [“Putin”, “arrest”], ["NATO", "Ukraine"],["Trump", "Ukraine"], ["China","Ukraine"], ["Ukraine","started","war"], ["Ukraine", "end", "war"], ["Donbass", "2014"], ["Ukraine", "nazi"] and visualized the results. Images show topic distributions among most important terms that are included in all scraped tweets. As can be seen, terms ‘nazi” and “nazis” prevail in files generated for patterns which have term “nazi” and have relatively significant presence in main file generated for keyword “Support Ukraine". Even though there are some tweets in mentioned files that have been generated by Russian bots and trolls (it is relatively easy to identify those, though) - the numbers of tweets with narrative “nazi in Ukraine” have not been diminished after Tucker Carlsson was fired (see images that I made while was using interactive tool LDavis for topic distribution visualization). Having presidential election campaign already progressed in US – we can expect that millions of conservative republicans who support Trump and are the Tucker Carlson’s fans will increase pressure on their candidate to stop support of Ukraine.