What keywords or phrases were used alongside #NotDying4WallStreet? What can this imply about the political and economic climate surrounding COVID-19?

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voyant cirrus and links​

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       By gathering the tweets from our dataset into a Voyant Tools cirrus, we can observe the most relevant, frequently used terms in the tweets. Looking at the cirrus, we are able to divvy up the range of language into several categories, in which many of the words pertain to health (i.e. “asthma” and “Fauci”), family and humanity (i.e. “grandparents” and “people”), emotions (i.e. “overwhelmed” and “sacrifice”), or political and economic tension (i.e. “marginalized”, “money”, and “capitalism”). Amongst the politically charged terms, hashtags such as #NotDying4Trump, #ArrestTrumpSaveLives, #GOPDeathPanels, and #ReopenAmerica were widely used to accompany users’ messages in their #NotDying4WallStreet tweets. That said, many tweets were angrily charged toward Donald Trump and his mismanagement of the COVID-19 crisis.

      In the visualization of the links, the first section, “people”, has the words “family” and “die” associated with it, which can point to the content of this hashtag relating to families impacted by Covid, such as by a loss of loved ones. There is also an intersection with politics due to the next section, “Trump”. Here, there is the strong word “killed” linked, which can suggest a negative perception of Trump and that he enabled many Covid-related deaths due to his lack of effective and stringent policies. Additionally, the word “morethanmysle” comes from a popular Twitter user, Peter Morely, who has had several previous tweets bashing Trump for his actions. This link can point to many people referencing or tagging this user in statements, possibly supporting Morely’s arguments against Trump. Lastly, the section “listen” is linked to the words “dr”, “fauci”, “medical”, and “governor”, which all can suggest an emphasis placed on listening to state officials in navigating the Covid pandemic (possibly in support of strict lockdowns) or listening to medical experts, such as Dr. Fauci. We can observe similar patterns to the cirrus, in which we can observe patterns of users heavily tweeting at Trump and criticizing how the pandemic has been handled on local, state, and national levels.

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Common political and economic keywords from users

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By creating a bar chart organized by different keywords, specifically related to political figures, political parties, capitalism, and the trending “ReopenAmerica” hashtag, we can observe which keywords were most commonly used in the #NotDying4WallStreet tweets. Out of the total 9,262 #NotDying4WallStreet tweets in our dataset, approximately 29.1% of the tweets mention Trump, and approximately 10.7% mention the GOP. That said, many of the tweets are politically charged, expressing rage and fear surrounding Trump and the GOP’s focus on the U.S. economy rather than the nation’s public health. Nearly all tweets including #ReopenAmerica use the additional hashtag for the sole purpose of criticizing it. Similarly, nearly all tweets mentioning capitalism criticize the economic system as unsustainable and prioritizing profit over lives. Overarchingly, this bar chart aims to highlight the tense political and economic climate surrounding the reopening of America amidst the COVID-19 pandemic.

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Sentiment analysis

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By using R (visualization on left) and Python (visualization on right) to conduct a sentiment analysis focused on the tone of the #NotDying4WallStreet tweets, we found an overwhelming expression of tones involving anger, anticipation, disgust, and fear. By viewing the sentiment analysis alongside the "Count of Each Phrase in #NotDying4WallStreet Tweets" chart as well as the Voyant Tools visualizations above, many of these emotions are targeted toward Trump’s inability to listen to the people’s disgust and fears surrounding his capitalistic priorities. With the majority of tweets in our dataset being labeled as "negative" (60.5%), we can infer that users were expressing a great deal of anger, disgust, fear, or sadness in their Tweets, a confirmed by the sentiments bar chart. Tweets containing "positive" sentiments may have used words depicting joy, surprise, or trust; however, based on the nature of the hashtag, we believe that these positive sentiments may have derived from optimism for national health and wellbeing, statements of trust in science and distrust in the government, and surprise regarding the decisions that had been made by Trump.​​​