{"id":2605,"date":"2017-03-03T16:58:36","date_gmt":"2017-03-03T21:58:36","guid":{"rendered":"http:\/\/commons.trincoll.edu\/amst-data-driven\/?p=2605"},"modified":"2017-03-03T16:58:36","modified_gmt":"2017-03-03T21:58:36","slug":"48-hours-of-muslimban","status":"publish","type":"post","link":"http:\/\/commons.trincoll.edu\/amst-data-driven\/2017\/03\/03\/48-hours-of-muslimban\/","title":{"rendered":"48 Hours of #muslimban"},"content":{"rendered":"

I chose to narrow my data to the days following the muslim ban. I then further narrowed my data by breaking it up to every 6 hours.Through my data I am hoping to see terms in relation to \u00a0what people are doing to stop the muslim ban and how this is influencing people’s opinions of Trump.<\/p>\n

My voyant shows 8 documents with 191,086 total words and 14,534 unique word forms. The longest document in length was from February 4th between the hours of 7 PM and 11 PM. At first I was surprised by this because I felt like longest document would have been closer to when the ban was implemented. However, as I began to analyze this I realized that in some ways it makes sense because more opinions were able to form as time passed. The most frequently used word was muslimban and the second was Trump, which wasn’t surprising. What stood out to me after taking away the first set of stop words was that many tweets also included the hashtag nobannowall. I think this is important to note as people are already coming up with slogans to stand up against Trump. I was expecting to see a high count of the countries that are being banned such as Syria and Iraq \u00a0in my term list however they weren’t included. The character\u00a0\u00fb_ showed up in my data 4,642 times which I thought was pretty obscure. Also the character\u00a0\u0434\u0443_ was commonly found in my data. These characters were taking away from my data as they aren’t real words therefore I felt the need to add them to my stop word list.<\/p>\n

The stop words I added to the list were\u00a0\u00fb_,\u00a0\u00a0\u0434\u0443_, just, le, asked, and like because they have no relevance to my data and were taking up too much room in my corpus. I included 95 words in my corpus.\u00a0There were more negative words when it comes to Trump’s policy than positive ones. I think my word cloud suggests topics that are fairly obvious as some of the common words one would associated with a travel ban such as passport, immigration, and unconstitutional. I noticed that Seattle was a commonly tweeted word, which I thought was pretty random, so I looked up on google Muslim Ban in Seattle. The first results showed that on February 4 a judge in Seattle blocked the muslim ban all together. This was a big deal because he was one of the first to stand his ground against Trump. I also think its important to note that the word “can’t” was used many times. Even though this word is so simple it shows that the muslim ban is implementing the idea of failure and not supporting the idea that anyone has the ability to accomplish anything. My word cloud supports my analysis that more people are against the ban than for it. The most frequent used term is “muslimban,” which I think is important because that is the reason why my hashtag is currently trending. The second most commonly used word is Trump, which also makes total sense because he is the reason why the muslim ban occurred in the first place. The third most used word is judge. I find this interesting because this means many people were talking about the muslim ban in relation to how other legislature views this policy. The fourth most commonly used word is Muslim. This also doesn’t surprise me as they are the people who are being directly impacted by this policy. The fifth most common word is nobannowall, which as I said before is important because people are already grouping two policies Trump is trying to enforce and making a phrase to reject these policies.\u00a0\"Screen<\/p>\n

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I am picking Trump, judge, nobannowall and theresistance as my terms with high frequencies. The reason why I am picking these terms is because I feel like I will get a wide arrange of data in relation to judge as people would only tweet about it\u00a0when something becomes relevant in the news. I also want to see if there is any relationship between nobannowall and theresistance terms.<\/p>\n

.\u00a0\"Screen<\/p>\n

This pattern does not surprise me as like I said from February 3rd at about 11PM to February 4th at about 5 am there was a significant increase in the tweets involving the term judge. From this I can infer that a judge made a comment or even refuted the muslim ban in those hours. The term theresistance seemed to increase as the hours went by between February 3rd and February 4th. This makes me think that more people started\u00a0to band together as they were beginning to hear about Trump’s new policy. Another thing I found interesting was that there was a\u00a0dip in the term nobannowall on February 4th from 11 am to 4 pm and then an increase in the hours to follow. I had trouble finding on the internet if there was a specific reason for this. In relation to Trump he remained pretty consisted throughout the two days I looked at. However I wasn’t shocked by this as he is the main subject surrounding my hashtag.<\/p>\n

http:\/\/voyant-tools.org\/?corpus=79f5f053251bc9f50e14f868768c85ca&stopList=keywords-89abce7be6d587b613672a25ab6f4a00&panels=corpusterms,reader,trends,summary,contexts<\/p>\n

I picked Trump’s Executive order or what he calls “protecting the nation from foreign terrorist entry into the United States.” I thought that after conducting this text analysis I would be able to see if there are more differences than similarities between the two. The words I put on my stop list include shall, date, days, section and report as they don’t have much relevance to the story I am trying to tell. This time I have 55 words in my word cloud. There is some overlap when it comes to my two text analysis. For example refugee and immigration are both used. However, although they are talking about the same concepts I think the people on twitter are trying to get a different point across in relation to these terms. I also think it is interesting that one of the most common words used in Trump’s executive order is united, as his policy is actually causing a rift in society. I feel like Muslim should be included in the word cloud of his order because even though he doesn’t mention them that is who he is targeting in relation to his ban. This will help me read my twitter data better as I will now compare the words in his actual order to what people are saying on twitter.<\/p>\n

\"Screen<\/p>\n

Tufte approaches data visualization in a very structured way as he believes it is all about method and analysis. On the other hand Yau approaches data visualization in relation to how it is contextualized in the real world. I agree with Yau as data is more than just a graph on a piece a paper. Data should be looked at in terms of real life and how it is influenced by everyday decisions and outcomes. Yau’s approach can be shown through my lab as I was able to look at the most common words in my data and put them into context by searching the web. I was also able to see if events\u00a0in the news correlated to the frequency of that word throughout the course of a day. This \u00a0helped me understand my data better as I was able to look at it from a different standpoint and analyze what is making up #muslimban. The text analysis also allowed me to make new connections and link my data to what has happened in the past and what is happing right now.<\/p>\n

Work Cited:<\/p>\n

Tufte, Edward R. 2011. \u201cVisual & Statistical Thinking: Displays of Evidence for Making\u00a0Decisions.\u201d\u00a0Envisioning Information<\/em>, 27-54. Cheshire, CT.: Graphics Press.<\/p>\n

Yau, Nathan. 2013 \u201cRepresenting Data.\u201d\u00a0Data Points<\/em>, 91-134. Hoboken: Wiley.<\/p>\n","protected":false},"excerpt":{"rendered":"

I chose to narrow my data to the days following the muslim ban. I then further narrowed my data by breaking it up to every 6 hours.Through my data I am hoping to see terms in relation to \u00a0what people are doing to stop the muslim ban and how this is influencing people’s opinions of…<\/p>\n","protected":false},"author":1966,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2605"}],"collection":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/users\/1966"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=2605"}],"version-history":[{"count":2,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2605\/revisions"}],"predecessor-version":[{"id":2715,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2605\/revisions\/2715"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=2605"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=2605"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=2605"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}