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<\/a><\/p>\nAlthough there are only four dates to choose from, I decided to go ahead and graph them with a little bit of knowledge as to the drastic increase in numbers on the 9th and 10th of February. \u00a0I know there was a primary around this time and would not at all be surprised to see that it occurred on either the 9th or the 10th. \u00a0Throughout this entire project I have found there to be an extreme influx in tweet numbers when there is a a primary or debate. \u00a0These events clearly spark the interest of citizens who take to Twitter to express their beliefs and rally others around their candidate of choice.<\/p>\n
Just as I had expected, on February 9th, the New Hampshire Primary took place which was one of the most important and influential primaries in the country. \u00a0This is undoubtedly a reason for such a drastic increase on the day of the primary, as well as the following day where there was much discussion and delight for the winning candidate.\u00a0\u00a0I think that the following day #TrumpTrain was used in excess because Donald Trump won the primary. \u00a0Since this hashtag is most often used in support of this candidate, it is not surprising at all as to why there were nearly five times as many tweets as their were on February 8th. According to a New York Times article, “Mr. Trump, the wealthy businessman whose blunt language and outsider image have electrified many Republicans . . .\u00a0also tapped into a deep well of anxiety among Republicans and independents in New Hampshire, according to exit polling data, and he\u00a0ran strongest among voters who were worried about illegal immigrants, incipient economic turmoil and the threat of a terrorist attack in the United States” (New York Times, 2016.) \u00a0This further proves how the support for Donald Trump was at a high point following the primary as well as explaining the characteristics his followers believe in. \u00a0I want to include this final quote simply as a way to express the type of candidate Donald Trump truly is, “‘I am going to be the greatest jobs president that God ever created,’ vowed Mr. Trump, adding that he would ‘knock the hell out of ISIS,’ or the Islamic State” (New York Times, 2016.)<\/p>\n
In looking specifically at my own data, my number of tweets per day was definitely on the higher end of in comparison to the class data, in fact it was the highest. \u00a0I personally collected roughly 13,391 tweets per day while the class mean was approximately 2,893 per day. \u00a0The median was approximate 746 tweets per day. \u00a0What this means is the data was skewed to the right as the data lower than the median were closer to the median than those that were higher. \u00a0My data, along with #MakeAmericaGreatAgain were certainly outliers as they were roughly 11,000 more data points than the class mean. \u00a0Unfortunately, the mode was unable to be calculated because no one had the exact same amount. \u00a0I think it would have been extremely interesting if there was a mode to see how those two topics were interconnected. \u00a0I have found it fascinating after reading Tarleton Gillespie’s article, “Can Algorithm Be Wrong?” by how much Twitter and other media outlets can control and regulate material we can see and analyze. \u00a0One clear example from the reading where this happens is with reference to #occupywallstreet. \u00a0Gillespie states, “. . .\u00a0when tweets using the term #occupywallstreet seem to spike, the term did not Trend. Some suggested that Twitter was deliberately dropping the term from its list, and in doing so, preventing it from reaching a wider audience” (The Atlantic, 2012.) \u00a0It really makes me wonder how much material and data is censored. \u00a0The presidential candidates use social media as an enormous platform to gain support. \u00a0Would it be possible\u00a0for Twitter to single-handily censor tweets and determine who gains support in important elections? \u00a0This is a very real possibility and we must be aware that this could in fact happen. \u00a0The access to data is incredibly limited and it is of utmost importance to fully understand that this occurs.<\/p>\n
I thought that it was really incredible how varied the class data was in total. \u00a0My data on the class spreadsheet is limited as it was only four days out of my total data set because of of capacity overload on my GoogleSheet and the time it took me to export the files. \u00a0Throughout this entire project I have constantly been collecting data. On my Google spread sheet I hit a total of 117,000 tweets before it would refuse to collect more. \u00a0This is a clear example of how popular the presidential election is to talk about on social media. \u00a0I really was not surprised to see that this was the case because there is constant attention on the presidential race, especially during the four days I selected to examine. \u00a0I think that this means my hashtag is being tweeted and retweeted constantly, most often in support for Donald Trump. \u00a0Further, there #MakeAmericaGreatAgain was only 1,000 tweets behind per day. \u00a0These two numbers, when examined in conjunction point to the fact that Donald Trump has an extreme media presence and is being discussed constantly. \u00a0it makes me wonder if he has intended this to happen and is using it to gain support and momentum throughout his campaign. \u00a0Unfortunately, at the time of this writing the data for #FeelTheBern was not uploaded to the class data GoogleDoc. \u00a0I think it would be fascinating to compare this hashtag in support for Bernie Sanders with those in support of Donald Trump. \u00a0This may allow for a greater understanding of social media and how candidates use it to their advantage. \u00a0<\/span><\/p>\nThe only thing that this class data reveals to me is how popular and seemingly unlimited my data truly was. \u00a0I wish that I could have used all of my data for this portion of lab because I think that more days of collection, and more data in total would have allowed me to analyze #TrumpTrain more extensively. \u00a0Before this lab I never realized how much data I had actually collected and now knowing this and looking back I wish I had taken more time to find a way to extract all of my data from the GoogleSheet. This would have allowed me to examine it further and make a hypothesis with more certainty and a more positive outlook as I drew conclusions.<\/p>\n
______________________________________________________________________________________________________<\/p>\n
Healy, Patrick, and Jonathan Martin. “Donald Trump and Bernie Sanders Win in New Hampshire Primary.” The New York Times. February 09, 2016. http:\/\/www.nytimes.com\/2016\/02\/10\/us\/politics\/new-hampshire-primary.html.<\/p>\n
Gillespie, Tarleton. 2012. \u201cCan an Algorithm Be Wrong?\u201d
Limn<\/em> (2).<\/div>\n","protected":false},"excerpt":{"rendered":"After inserting the COUNTIF function, the total number of\u00a0tweets in English was 52,680. The total number of tweets was\u00a053,564, therefore the percentage of tweets in English is roughly 98.8%. I was not surprised at all by the extremely large number of tweets in the English language. \u00a0This number is in direct relation to the…<\/p>\n","protected":false},"author":1630,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[6],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/1040"}],"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\/1630"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=1040"}],"version-history":[{"count":7,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/1040\/revisions"}],"predecessor-version":[{"id":1201,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/1040\/revisions\/1201"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=1040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=1040"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=1040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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