https://databoytim.carto.com/builder/a3cfd978-f45d-11e6-9c7b-0e98b61680bf
I expect to see a lot of retweets because the big Twitter accounts are retweeted very often whenever they make a political tweet. The people are the masses, and there being brainwashed by the voice of reason.
My most frequent word is MakeAmericaGreatAgain but I deleted it! Now the main words are The_Trump_Train, RealDonaldTrump, maga, and America. The first two are both twitter accounts that were retweeted many times
It stands out to me that Ivanka is one of the popular words in the cloud, this means a lot of people are tweeting about the first lady.
I think the usernames give a lot of information about the topic because although they don’t give specific details, the fact that they are there so often means that they are ‘reputable’ sources for Trump supporters. Some words that I do not think are helpful are: potus, america, and maga. They don’t add anything new and give any more information to the data scraper. The word ‘excellent’ is a good one though, because it offers up the general opinion of the president by his supporters.
The stop words I added (in addition to the ones on the sheet) were:
retweet, some twitter names that were gibberish, and a bunch of random letters with symbols above them. I had around 10 random letter/symbols to remove.
I put 55 words in my word cloud! I thought it looked good and was informational like this.
I expected to see more words that were about the quality of job that Trump was doing, and even a few negative words. The words were majorly positive. This reveals that the people tweeting with the #makeamericagreatagain hashtag are mainly all supporters of Trump (which makes sense.. why else type out the long hashtag). I was surprised by all the different language letters in the word cloud, but I guess a lot of Germans are probably tweeting about our president. The key terms that make up the word cloud are Trump, president, potus, and other description words of our president. There are no subtle hints in the word cloud, everything is rather straightforward. This doesn’t really change how I view the data or the hashtag. As expected, many people tweeting with my hashtag are pro trump.
Im choosing the words: attorney, white, and Americafirst
I think the word attorney is popular because Donald Trump fired his attorney general when she refused to defend his immigration ban. This was a very hot topic in the beginning of February, and it makes sense that it reverberated around the twitter-sphere. I chose white because that is what the idea of Trump’s America is. Straight, white, men ruling the country. Lastly I chose Americafirst because I didn’t know what this meant, but I thought it had to do with Trump putting the needs of Americans above those of foreign countries.
Like I thought, Attorney was very hot at the beginning of my week and it died down as people stopped caring / tweeting about it. In today’s day and age, things can go viral instantly and die out just as quickly. White has stayed consistent and that makes sense as well, because white will be a constant theme of America during the Trump era. AmericaFirst is peaking at the end of my week, which I don’t understand. I couldn’t find the floppy disc save section so I just included the actual link.
http://voyant-tools.org/?corpus=4375e29de27244c05ccea210660c3f91
Part 6:
I found the article titled: “Will Eliminating Corporate Transparency Make ‘America Great Again?”. I chose it because it fit in with the MAGA hashtag and was very recent. I was also curious to see some policy and opinions in action.
This dataset shares similar words such as American, country, etc. but it differs because the second cloud is much more specific and has ‘action’ words. It connects to the American aspects of my dataset for sure. This doesn’t really help me understand my data set more, but it does give a look at a specific case study that has to do with my hashtag.
I am a fan of Yau’s approach (#TeamAsian) because I believe it takes into consideration the fact that data is not everything. Yau, like many leaders in our data driven society, loves collecting data about trends, people, and communities. Yau’s approach differs than the typical one though, because he acknowledges that there are some limitations to the amount of information we can gather about a person. It has been a common theme in our class that collecting data about a person can tell us many things about them, but it does not nearly give us a full picture of their life. A person will always be more than points on a graph, or the things they buy, or their web searches. Yau’s belief in the limitations of data are concurrent with my own. Data can help us draw a picture, but it cannot tell the whole story. Using an example from today’s word clouds, the popular words were seemingly positive and no negative words made it into the cloud, but that doesn’t mean there are no negative tweets or feelings about Trump in the US. By only looking at the data that is visualized, we are missing important aspects of the American collective identity.
References:
Broadman, Harry. “Will Eliminating Corporate Transparency Make ‘America Great Again’?” Feb 28, 2017. Forbes Magazine.
Yau, N. (2013). Data points: visualization that means something. Indianapolis: John Wiley & Sons.
#TeamAsian unite,
Your text analysis confirms my prediction that mostly people who supported Trump used #makeamericagreatagain. I’m not surprised that “americafirst” trended as well because I originally wanted to analyze that tweet and its ideology seem to resonate with much of the most economically struggling communities in the U.S. I did not know that Trump supporters used the unique term “Trump Train” to describe themselves. It just shows that each group wants to make a presence on Twitter. I’m quite surprised that your text clouded included many positive general such as “excellent,” “great,” and “congratulations.” Do you know how these terms referred to Trump or any policy that he implemented? I’m surprised that Ivanka, Trump’s daughter, trended more than Melania, the first lady; it shows the unconventional roles that each Trump family member has – Ivanka seen as more of a policymaker than Melania even when traditionally, the first lady implements more policy than the President’s children.
I am interested in seeing how the text analysis cloud may have changed since the very first days that you collected the tweets until now. It may help you see how the strength of Trump’s support base on Twitter. If the clouds are similar, it may indicate that Trump supporters on Twitter are his most loyal supporters. If the clouds are different, is it because the decline of Trump’s approval rating?
Your text analyses made me realize how formal the media is compared to the general public. The difference between the clouds for the tweets and the article help me consider if people are using Twitter to express their true opinions since the media and academics do not necessarily cover on the issues or stories that people care about.