It seems as though many people are less active during the middle of the week.\u00a0 The least amount of activity was during both Tuesday and Wednesday. When looking at weekday versus weekend it seems as though people are tweeting more during the weekend.<\/p>\n
When looking at my data on voyage it displays that my corpus has 7 documents with 84,582 total words and 4,127 unique word forms.\u00a0 It was extremely interesting to look into the distinctive words and how they differed throughout each day of the week.\u00a0 On Some of the words that were popping up that pertained to my hashtag were ones such as \u201cplanned\u201d \u201cparenthood\u201d \u201chealth\u201d \u201caccess\u201d \u201cwomen\u201d \u201cprotecting\u201d. \u00a0When taking out terms such as https, t.co, RT and my hashtag the cloud of words became more condensed and more interesting. \u00a0The most frequent words that were used in the corpus were\u00a0planned<\/a>\u00a0(1770);\u00a0parenthood<\/a>\u00a0(1768)<\/span>;\u00a0ppact<\/a>\u00a0(1660);\u00a0care<\/a>\u00a0(1124);\u00a0speakerryan<\/a>\u00a0(597). \u00a0The first word that came up was planned and the second was parenthood. \u00a0Although my hashtag does not use these terms and simplifies it to “PP”, it is clear that this should be what is first and most commonly associated with my hashtag. \u00a0It was interesting to look at the hashtag #ppact as one trending that also pertains to planned parenthood. \u00a0The next terms that proceeded\u00a0I was interested in seeing that speakerryan came up and after looking on google I found that this was a reference to speaker Paul Ryan from the House of Reps. \u00a0When looking this up I found a CNN article regarding the topic-\u00a0http:\/\/www.cnn.com\/2017\/01\/05\/politics\/paul-ryan-planned-parenthood-obamacare\/ . \u00a0This article discusses how Paul Ryan announced that Republicans are moving to strip all of the federal funding for planned parenthood in order to begin their process to dismantle Obamacare. \u00a0It is occurrences such as this that I\u00a0thought would effect my hashtag and would be interesting to see the discussion generated during. \u00a0Some words that are high on the terms list are filler words such as i’m and say as well as \u00e2. \u00a0There are some words that I was not positive as to why they are on there and am unsure if they are words that are obscuring or if they have more of a significance that I am missing such as 0000, arizonans, and tue. \u00a0I was specifically interested in understanding why “arizonans” was included- as I tried to look up if there was any reason behind this but nothing popped up immediately.<\/p>\n The words that I decided to add to my stop words were:\u00a0i’m, say, 0000, \u00e2, amp,\u00a0\u00f0\u00ff, pin, asvrjq6g0j, ainnvq1jg9, t.c\u00e2, i\u00e2, npwf, jhiavvg\u00e2, 6r6y8aobqf, n6iymytyar, ista\u00e2, 700, 492, n6iymytyar, 6061, 608, nevadans, 202, 804, 8210, 720, istand\u00e2, r0ou7jl2hn, and d7v4\u00e2. \u00a0After taking these words out I feel as though it really cleared up my overall diagram, allowing me to accurately look at the way in which people are talking about my topic without many outliers. \u00a0After the first round of taking these words out I was still seeing a few extra words so I also took out\u00a0775,\u00a0608, and\u00a00136. \u00a0I feel as though in order to visualize the best story pertaining to the discussion around my topic around 85 words should be represented. \u00a0Although this does not seem like many it allows for one to really look at and understand these words, as I feel as though many of them have a deeper story such as the example of speakerryan. \u00a0Also, my hashtag seems to generate a lot of excess words and otherwise there would be a lot more stop words to add in. \u00a0When looking at 85 words there are many simple words that display the support from the overall community such as rights, need, health, urgent, sign, women, etc. Through this visualization it is also clear that many people are not only tweeting in support of planned parenthood, but tweeting towards those and that opposing and trying to distinguish planned parenthood. \u00a0There are words that target trump, speakerryan, etc. \u00a0I also found it interesting that there were different people and groups of people being targeted in these tweets such as emmastone, nevadans, arizonans, jefflake, elizabethbanks, texas, etc. <\/p>\n The words that I choose to look into were\u00a0“speakerryan”, “defund”, and “trumprussia”. \u00a0I decided to do speakerryan in order to see when people began tweeting about his ideas to take away federal funding for planned parenthood as well as how this has changed overtime. \u00a0I also decided to look at defund in order to see how it correlates with speakerryan as to when people were most tweeting about it. \u00a0The third word that I chose was “trumprussia” due to the intense discussion this topic would generate. \u00a0While I also understand this is a topic that would cause a lot of discussion, I am also really interested in understanding its relation to my hashtag. \u00a0This is why I decided to look at when this was being tweeted over time and see if I could draw any conclusion on its relevance as a key word within my topic.<\/p>\n When looking at this data it was interesting to see that speakkerryan was pretty persistent throughout, but started to decline later in the 7 day period. \u00a0I thought that defund would have spiked at the same time as speakkerryan, but was surprised to see that it did not. \u00a0I also was not surprised that there were not many tweets being generated about trumprussia throughout the period then all of a sudden there was a large spike on Tuesday. \u00a0The fact that there was not much talk about this keyword then it had a large presence explains why it was overall a trending term. \u00a0After scanning the three words tweet contents I decided to look into the contents of “defund.” \u00a0Most of these tweets contained the statement “tell<\/span>\u00a0the<\/span>\u00a0Senate<\/span>:\u00a0Block<\/span>\u00a0all<\/span>\u00a0attempts<\/span>\u00a0to<\/span>\u00a0defund<\/span>\u00a0#PlannedParenthood<\/span>.” \u00a0Other ones noted that Congress wanted to defund planed parenthood and would go on with hashtags and slogans to stand with it. \u00a0This is the type of discussion\u00a0I initially thought would be generated through my topic.<\/p>\n http:\/\/voyant-tools.org\/?corpus=ebc9e6b8d6d163eed757fc21c23de818<\/p>\n <\/p>\n The article that I chose to put into voyant was posted by time stating that “Republicans Take Upper Hand in Fight to Defund Planned Parenthood”. \u00a0I decided to choose this article due to the fact that my entire topic was established due to the political decisions to diminish planned parenthood. \u00a0Throughout my data scrapping it became clear that my tweets revolved around taking a stance around\u00a0most recent acts to take away federal funding from planned parenthood. \u00a0This article discusses how Republicans plan to take action\u00a0to defund planned parenthood, thus I thought it would be very relevant and of interest to see an\u00a0analysis of this post. \u00e2, oewe, 553, trump\u00e2, 07, 1.3, 1.6, 2018\u00e2, 300, can\u00e2, care.\u00e2, group\u00e2.<\/p>\n Overall I felt as though these stop words were coming up in the article less frequently than in my tweets. \u00a0When looking at my own tweets generated and comparing it to these they are very similar in the major terms. \u00a0Where it differs is when looking into the excess terms. \u00a0I feel as though the tweets display more of the opinions regarding my topic, whereas the article simply reports what is going on. \u00a0Because the tweets show the way in which this news is having an impact on the general population, I feel as though they are more interesting to look at when understanding my topic and my hashtag overall. \u00a0This being said, it is also possible that these tweets are slightly biased in the fact that most if not all of the time, those who are actively tweeting about this are clearly upset with what is occurring in the news.<\/p>\n http:\/\/time.com\/4626516\/planned-parenthood-defund-republicans\/<\/p>\n Examining Tufte’s statement that\u00a0\u201cSuperior methods are more likely to produce truthful, credible, and precise findings\u201d (1997, 27) as well as Yau’s statement that “Data is an abstraction of real life, and real life can be complicated, but if you gather enough context, you can at least put forth a solid effort to make sense of it\u201d (2013, 41), it becomes clear through my own personal readings as well as analysis that Yau’s approach is more sensible considering the fact that we are gathering data from twitter. Like I addressed in my last post when looking at both the words generated from twitter as well as a reliable news article, it becomes clear that there are many ways in which these words and this data can be biased based upon the context and platform it is on. \u00a0Even when looking at news articles, each company has a clear stance and because of this creates some sort of bias. \u00a0Because of this it becomes essential to really examine\u00a0context and understand data as more of a story. \u00a0I personally have worked a lot in advertising and in this we focus on combining both\u00a0data driven insights and\u00a0the creative story to attract the consumer. \u00a0When looking at it like this it becomes essential to see the ways in which data can be used to create and establish meaning based on its context.<\/p>\n","protected":false},"excerpt":{"rendered":" I think it would be interesting to follow these tweets through the week of February 6-13th.According to an article from the guardian, the next Women\u2019s March scheduled for March 6 and announced on Feb 6.\u00a0 https:\/\/www.theguardian.com\/commentisfree\/2017\/feb\/06\/women-strike-trump-resistance-power It seems as though many people are less active during the middle of the week.\u00a0 The least amount…<\/p>\n","protected":false},"author":1969,"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\/2468"}],"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\/1969"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=2468"}],"version-history":[{"count":5,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2468\/revisions"}],"predecessor-version":[{"id":2543,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2468\/revisions\/2543"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=2468"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=2468"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=2468"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"#standwithpp](\"http:\/\/commons.trincoll.edu\/amst-data-driven\/files\/2017\/03\/standwithpp-cloud.png\")
<\/p>\n![\"stand](\"http:\/\/commons.trincoll.edu\/amst-data-driven\/files\/2017\/03\/stand-with-pp-key-words.png\")
<\/p>\n![\"article](\"http:\/\/commons.trincoll.edu\/amst-data-driven\/files\/2017\/03\/article-term-cloud.png\")
<\/p>\n