{"id":2501,"date":"2017-03-03T11:22:10","date_gmt":"2017-03-03T16:22:10","guid":{"rendered":"http:\/\/commons.trincoll.edu\/amst-data-driven\/?p=2501"},"modified":"2017-03-04T20:54:38","modified_gmt":"2017-03-05T01:54:38","slug":"what-do-they-say","status":"publish","type":"post","link":"http:\/\/commons.trincoll.edu\/amst-data-driven\/2017\/03\/03\/what-do-they-say\/","title":{"rendered":"What do they say? The War on Words at the Border"},"content":{"rendered":"

I predict that the the words Muslim, Syria, refugees and terrorists, as opposed to the words Mexico and illegal, will dominate the text analysis. More people will argue that Syrian Muslims refugees are not all terrorists instead of defending the rights of illegal immigrants who currently live in the U.S. I also expect there to include many major airport codes (such as\u00a0 #JFK, #LAX) because a lot of the backlash revolved around Immigration and Custom Enforcement’s detainment of people who had valid visas in the U.S.<\/p>\n

My text data included 1,017,835 words and 49,876 unique words. On February 1st, the most common words in the tweets such as “nobanstudentvisa” and “penalties,” reflected uncertainty in Trump’s travel ban policy on the seven predominantly Muslim countries. Towards the end of the data collection on February 10th, I noticed a progression towards the discussion on how to deal with the policy: the most common words were “9th” and “circuit,” possibly referencing to the U.S. 9th Circuit Court of Appeal’s repeal of the travel ban.<\/p>\n

Since “Muslimban” was the most popular word, I correctly predicted that my hashtag referred more to Trump’s travel ban than it would on illegal immigration or general immigration issues in general. I am not surprised that the next popular words referred to Trump (including “trump,” “realdonaldtrump,” and “impreachtrump”), since the media symbolized the policy as a key indicator to Trump’s anti-immigrant sentiment. However, I did not realize the scope of how the policy impacted anti-Trump resistance movement. “Theresistance” and “resist” were the next most popular words, perhaps indicating not only discontent with Trump, but a will to defy his policies. For February 5th, the day of the Super Bowl, “boycottbudweiser” trended though it seemed conflicting to use the term with #nobannowall since Budweiser’s Super Bowl ad was pro-immigration. I extended my scale multiple times to see what other less frequent words were included in my tweets and am surprised to see no mention of “Uber” which supposedly tried to take advantage of a taxi strike in NYC after the travel ban and very little mention of “Syria” which the conflict there itself is not controversial with Assad, but since most refugees taken in by Western countries this year were from Syria. Several random Twitter characters that were used with no context in the tweets were included in my cloud including “\u00f0\u00ff,” “\u00f8,” and “\u00e2.” In addition, many terms were just part of the URLs of commonly retweeted links.<\/p>\n

List of additional stop words (or weird characters) added:<\/p>\n

    \n
  1. \u00f0\u00ff<\/li>\n
  2. \u00f8<\/li>\n
  3. \u00e2<\/li>\n
  4. nnedhxnc4h<\/li>\n
  5. ldjy0xf<\/li>\n
  6. t.c<\/li>\n
  7. https:\u00e2<\/li>\n
  8. qh4eg0geqt<\/li>\n
  9. k8whb86no2<\/li>\n
  10. 4o4873lyyf<\/li>\n
  11. mfmuagew\u00e2<\/li>\n
  12. 0iavlkoyxl<\/li>\n
  13. lt9wlx<\/li>\n
  14. t\u00e2<\/span><\/li>\n
  15. it’s<\/li>\n
  16. \u00f9<\/li>\n
  17. i’m<\/span><\/li>\n
  18. \u01ce<\/li>\n
  19. \u0101<\/li>\n<\/ol>\n

    My cloud has 235 terms. As mentioned before, the most popular terms described “Muslims” or referred to “Trump.” Since the most popular term was “muslimban” and other relatively popular terms such as “nomuslimban” and “muslim” were related to the term,\u00a0 it indicates that #nobannowall became popular in relation to Trump’s travel ban and not necessarily on his rhetoric on the proposed U.S.-Mexico border wall (or not yet). The high usage of terms like “Trump” and “realdonaldtrump” reflects that the hashtag refers anti-Muslim immigration policies of only Trump – not of other U.S. or world leaders. Though from my previous labs, I inferred that the hashtag was used to demonstrate outrage against Trump’s policy, I did not realize users expressed such as strong resistance against Trump from the hashtag. “resist,” “theresistance,” and “resistance” indicate that not only did people object to the policy, but this policy was intolerable to the point that Trump had to be stopped. It does make sense because it seemed that “protests” and “bodegastrike” (which was in NYC) were integral to this resistance process. As someone who did not follow the Super Bowl as much (though I did watch most of the game), I was surprised that people would use “superbowl” and “boycottbudweiser” so much. It made me think about how popular culture can be used (ex. Budweiser ad on immigrants) can generate awareness and controversy on current political issues. I also realized that people can also criticize popular culture for not doing enough to deal with societal issues. The five most frequent terms “muslimban,” “theresistance,” “resist,” “trump,” and “ban” relate to the main points and characters involved in travel ban policy including Trump (person who carried out the policy), Muslims (people who were most affected by the policy), and the resistance (people who oppose the policy and took action through protects and dialogue).
    \n<\/strong><\/p>\n

    \"Cloud\"<\/p>\n

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    “Bodegastrike,” “protest,” and “stonewall” interest me the most. Since they refer to physical protests, I am interested in investigating the scale of protests, including how many and how long were the protests. Though I may not get this answer from the text analysis, I want to know if people only tweeted about the protests or did people use Twitter to document their participation in the protests. I want to compare the group of protest-related terms with terms related to the legal implication of the policy including “circuit” and “ACLU.”<\/p>\n

    \"Word<\/p>\n

    In the first two days (after the announcement of the travel ban on January 30th), all of the protest-related terms were more popular. Towards the end of the data collection (about 6-10 days after the travel ban announcement), the legal-related terms began to become more popular and reached the same popularity of the protest-related terms during the first few days. At the same time, the protest-terms had lost almost all of its traction. These trends backed up my logic on the process of activism. First, there is “chaos” on the issue and everyday people organize public, physical protests against the policy. Then, the media covers the protests and people who are affected by the policy, leading to more awareness by the general public and people of more power. This awareness leads to legal action. As legal aid organizations such as the ACLU bring lawsuits courts such as 9th Circuit Court of Appeals, protests die out as people trust the legal system has the power to take stronger action than they can.<\/p>\n

    Link to corpus<\/strong>: https:\/\/voyant-tools.org\/?corpus=cb9acff20e9e2070d6e820a44fe5e904&stopList=keywords-0cab6a0bd242aa5d0fef520d8295ed0b&panels=cirrus,reader,trends,summary,contexts<\/p>\n

    I selected a feature article on the issues of illegal and legal immigration on the U.S.-Mexico border from the Migration Policy Institute because it emphasizes that not just “Mexicans” (who Trump blame) cross the U.S.-Mexico border but other groups that we do not commonly think of such as the Haitians, Cubans, Asians, and Africans as well. Since I could not find a feature article or policy on the recent travel ban, I felt that this article reflected on the “wall” component of my hashtag and the relative diversity of those who are affected by immigration policies as shown the aftermath of Trump’s travel ban. As shown in my cloud for the article below, the article certainly reflects upon the diversity of immigration in America and the implications that a travel ban or a border wall has on the international relations with various countries. Based on the popular words, I feel that the article does not necessarily explore the rights that immigrants have when they are in the U.S. or are trying to come back to the U.S., but more of how American immigration policies play a wider role in international politics. Compared my Twitter data set, the cloud explores how people are coming to the U.S. (by boat, walking, etc.), types of entry (ex. illegal, certain visas, refugee status), and then, who is actually coming (ex. Cubans, Mexicans, Haitians, Asians, Africans).<\/p>\n

    List of stop words:<\/p>\n

      \n
    1. fy<\/li>\n
    2. figure<\/li>\n
    3. and<\/li>\n
    4. because<\/li>\n
    5. likely<\/li>\n<\/ol>\n

      \"migrationinstitute\"<\/p>\n

       <\/p>\n

      Tufte uses examples from John Snow’s investigation on the cholera epidemic in London and the Challenger shuttle accident to show that having data is not simply enough; researchers must organize their data in a way to prove that there is a correlation between the situation and their theory and present their data so that people can feel that their theory is correct. Yau shows that there are various ways to showcase your data and each method (ex. dot graph, line graph) may be better or worse to represent your data.. The text analysis lab proves that Tufte’s methods are better because using the cloud does not necessarily demonstrate the exact relations between a topic\/term and our hashtag. If John Snow just showed his map of the people who died of the cholera in London, it would not necessarily prove his theory that the epidemic started from the contamination of a well. In order to further prove my point that #nobannowall articulated backlash against Trump’s policy, I showed a frequency graph showing the process that ultimately led to the repeal of the policy by the 9th Circuit Court of Appeals. Also, by deleting weird characters from my cloud, I was able to eliminate irrelevant terms that would have obscured the understanding of my data which was not effectively done during the launch of the Challenger Shuttle.<\/p>\n

      Works Cited<\/strong><\/p>\n

      Bolter, Jessica. Feb. 16. “The Evolving and Diversifying Nature of Migration to the U.S.-Mexico Border,” 2016, http:\/\/www.migrationpolicy.org\/article\/evolving-and-diversifying-nature-migration-us-mexico-border<\/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 predict that the the words Muslim, Syria, refugees and terrorists, as opposed to the words Mexico and illegal, will dominate the text analysis. More people will argue that Syrian Muslims refugees are not all terrorists instead of defending the rights of illegal immigrants who currently live in the U.S. I also expect there to…<\/p>\n","protected":false},"author":665,"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\/2501"}],"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\/665"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=2501"}],"version-history":[{"count":40,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2501\/revisions"}],"predecessor-version":[{"id":2750,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2501\/revisions\/2750"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=2501"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=2501"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=2501"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}