{"id":2545,"date":"2017-03-02T15:12:03","date_gmt":"2017-03-02T20:12:03","guid":{"rendered":"http:\/\/commons.trincoll.edu\/amst-data-driven\/?p=2545"},"modified":"2017-03-02T17:20:39","modified_gmt":"2017-03-02T22:20:39","slug":"a-web-of-resistance","status":"publish","type":"post","link":"http:\/\/commons.trincoll.edu\/amst-data-driven\/2017\/03\/02\/a-web-of-resistance\/","title":{"rendered":"A Web of Resistance"},"content":{"rendered":"

Part II<\/p>\n

I am hoping that the tweet content will be about the recent news, announced on February 6th<\/sup> in The Guardian<\/em>, about the second women\u2019s march that will occur on March 6th<\/sup>. I chose the week of February 6th<\/sup>-13th<\/sup> because I think there will be a lot of positive support for the march. Most of the tweets wil be personal reasons about why they are in support of the march and of women\u2019s rights in general.<\/p>\n

Part III<\/p>\n

My Voyant shows 8 documents, with a total of 72,062 words and 6,907 unique words. The longest documents in length are February 9th<\/sup>, February 8th<\/sup>, and February 6th<\/sup>. I would have first assumed that February 6th<\/sup> would be the longest document, since that was the day that the second women\u2019s march was announced. However, it makes sense that the longest document is three days later, because by that time the news would have traveled around and more people would have opinions to share. It is interesting also to see that one of the most frequent words used in the documents combined is maga<\/strong>, since most of the tweeters I would assume are against President Trump. Resist <\/strong>is also a frequently used word, which makes more sense with my hashtag. It is interesting to see the two contradicting words have the same amount of frequency, around 450.<\/p>\n

I first thought that tcot<\/strong> would be a Stop Word that I needed to add to get rid of. After clicking on it, I realized that it appears 383 times! So I checked out the meaning on Twitter, and found that it actually stands for Top Conservatives on Twitter. Now I know that I definitely should not delete that! I also see the word hey <\/strong>used often in the visualization. I am going to put that as a stop word because I do not see the necessity to have it be a part of the visualization. Other words like mt, hr, _\u00f9, it\u2019s, _\u00f9\u00f5, kue5azbeo0, \u00f9\u00e0\u00fchttps, 0gbbs95cqb, heyg61so60, hh4pp, \u00ef\u00ad__\u00fa, vj0j1xvxvy, \u00fc, \u00fb, \u00f9\u00ee\u00f9_\u00f9\u00ee\u00f9, htt, ht, \u00e8, \u00e0, _\u00f9_. \u00a0<\/strong>I added them all to my Stop Word list because they are all just symbols or meaningless words which do not add any meaning to my data visualization.<\/p>\n

To depict the best story possible of my #WhyIMarch, I chose a visualization that shows 145 tweets. On my screen womensmarch<\/strong> is the largest and longest word shown. I think it is important to keep\u00a0that word in my picture because my hashtag is trending due to the women\u2019s march, so it makes sense that it is the most used accompanying word.<\/p>\n

Overall, most of the words that I see in the visualization are what I expected to see. As I mentioned earlier, womensmarch<\/strong> is the first term to grab your attention. The second and third most common words are resist <\/strong>and maga.<\/strong> These two are actually quite contradictory together for my topic. I would expect to see resist<\/strong>, due to the backlash against President Donald Trump\u2019s statement on anti-women. It makes sense to use #resist and #WhyIMarch in the same tweet. However, I do not quite understand why maga<\/strong> is the third most used word. Women marching in the women\u2019s march on January 21st<\/sup> were protesting President Trump\u2019s statements against women\u2019s rights so I am confused as to why people tweeting about #WhyIMarch would use another hashtag in support of the President. This makes me think that maybe #maga does not stand for #MakeAmericaGreatAgain, but #MakeAmericaGayAgain. This could be one answer to why people use the #maga hashtag along with #whyIMarch, to resist against President Trump and to show that they are against Trumps discriminatory statements. The fourth most tweeted word along with my hashtag is america_partii<\/strong>, which I had to google to understand. My google search took me to the Twitter profile of @AMERICA_PARTII with the name \u201crefoundingFather\u201d. As I kept reading, a lot of the other hashtags used are #trump #buildthewall #tcot #draintheswamp #patriotparty 4ppl, which leads me to understand that there is a new movement on Twitter to start a new political party in America. This #america_partii concept has me a little confused about the relevance to the women\u2019s march, because every tweet seems to be Pro-Trump. I think there is a possibility that this user was created to make fun of the Pro-Trump Twitter movement, and use the hashtags that Trump uses himself to emphasize what the user is marching against\u2026 The next three hashtags all relate to the idea that they are being used to make fun of Pro-Trump movements, like tcot<\/strong>, draintheswamp<\/strong>, and trump<\/strong>. Caroline and Danny, I am interested to see what you both think about this contradiction. The next most used words\u00a0are very pro women\u2019s march, such as shepersisted<\/strong>, whyIresist<\/strong>, and woman<\/strong>, which are all the types of words that I expected to see.<\/p>\n

\"Visualization\"<\/p>\n

Part IV<\/p>\n

To me, resist<\/strong>, shepersisted<\/strong>, wefightback<\/strong> and hearourvoice<\/strong> stand out most to me. Women\u2019s rights and the women\u2019s march is a very important topic in this day and age. As a country we have made strong progress in human rights. 1919 especially was a huge year in our country when women were granted the right to vote. Since then, women\u2019s rights have been on an upward spiral. Women have been outperforming men in many fields as there are many more movements against sexism in the world now more than ever. The election of President Donald Trump has caused a panic from the movement. Women are feeling targeted by his political statements in office and use Twitter as an outlet to share their thoughts and opinions. The words I chose all signify the power of woman in America. Women will not let this bully take away from the advancement that we have made as a country, and these words emphasize the power an individual can have when they all come together.<\/p>\n

Part VI<\/p>\n

\"Screen<\/p>\n

I am not surprised to see a rise in tweets in #shepersisted, #resist, and #wefightback after February 6th because that is the time that it was announced that there will be a second women’s march. Particularly #shepersisted and #resist skyrockets on February 9th-11th. This is interesting due to the fact that this particular weekend was Presidents Day Weekend. In class, Professor Geisking pointed out that most tweets occur during the week day, however from this graph, I can see that there is a large rise in tweets using the given hashtags over that\u00a0weekend, and seems to go down after Sunday.<\/p>\n

Part V<\/p>\n

http:\/\/voyant-tools.org\/?corpus=6904d842b3a58f0fb06de9bd12b5ace0&stopList=keywords-34db5b7b5dee8e9bfd3f0ab411b1c54c&panels=corpusterms,reader,trends,phrases,contexts<\/p>\n

Part VI<\/p>\n

I selected a PDF titled “Women’s March on Washington: Guiding Vision and Defining of Principles”. This document is 6 pages long and outlines the key overview and purpose of the march. One of the paragraphs is titled #WhyWeMarch, which outlines bullet points of paragraphs describing why people are marching for all over the world.\u00a0This document is probably the most relevant document I could have found to write about for my hashtag #WhyIMarch and I am excited to see what I find through Voyant.<\/p>\n

I used the following as stop words because I did not see the relevance in having them in my visualization regarding my topic, women\u2019s rights.<\/p>\n

Stop Words: bodies dr air c\u00e1ceres deputy deweever farm faster gaylynn huerta neuwirth o\u2019neill rollbacks sieck wong<\/p>\n

\"Screen<\/p>\n

https:\/\/static1.squarespace.com\/static\/584086c7be6594762f5ec56e\/t\/587ffb20579fb3554668c111\/1484782369253\/WMW+Guiding+Vision+%26+Definition+of+Principles.pdf<\/p>\n

What I am seeing in this data visualization set is very big on the democracy that America entails. The\u00a0most frequent words used in the document are\u00a0women, rights,\u00a0believe,\u00a0justice,<\/strong>\u00a0and access<\/strong>.\u00a0<\/strong>This document\u00a0recognizes the multitude of social justice and human rights issues, and highlights the importance of resistance and self-determination moving forward. Most bullet points defining why people march begin with “We believe” or “We recognize”, emphasizing the importance\u00a0of democracy. This format relates back to Constitution, which addresses the world as “We The People…”. This data set does not really change my opinion on my Twitter data up until this point. There are similar words used in both visualizations, with women and women’s rights being the main theme. This dataset is much less catered to the women’s march but more generally about social equality and human rights.<\/p>\n

Part VII<\/p>\n

Tufte and Yau have two different approaches on data visualization. Tufte believes that the credibility to a data visualization has to do with the method and analysis. In other words, the effect\u00a0of analyzing data depends on the “quality of methods used in displaying and assessing quantitative evidence” (p.27). Yau on the other hand, talks about the importance of context for the data. I agree with Yau because I believe it is important to have real world context along with the dataset. One can analyze a graph or a chart, but in order to get the most meaning out of it, one needs to compare the data with real life context. For example, in this lab I found a PDF file of the Guiding Visions and Defining Principles of the Women’s March on Washington on January 27th. The visualization of this document compared to the visualization of my hashtag, #WhyIMarch, adds more real world context to the images. This, Yau argues, creates a stronger point, and not just an “abstraction of real life” (p.41).<\/p>\n

Work Cited:<\/span><\/p>\n

Tufte, Edward R. 2011. “Visual & Statistical Thinking: Displays of Evidence for Making\u00a0Decisions.”\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":"

Part II I am hoping that the tweet content will be about the recent news, announced on February 6th in The Guardian, about the second women\u2019s march that will occur on March 6th. I chose the week of February 6th-13th because I think there will be a lot of positive support for the march. Most…<\/p>\n","protected":false},"author":1537,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2545"}],"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\/1537"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=2545"}],"version-history":[{"count":4,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2545\/revisions"}],"predecessor-version":[{"id":2568,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2545\/revisions\/2568"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=2545"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=2545"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=2545"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}