Analyzing each graph against the group, the biggest difference I noticed was in the Gephi maps. Tim’s graph was very spread out with not many connections, while Jennifer’s was very connected and had some interesting groups such as the ACLU in hers. My graph had similar connections as Jennifer’s, but it was more group-focused. Each of our word splashes showed the connections to President Trump, with some differences. Both Tim and Jennifer’s graphs had direct connections to @POTUS, @RealDonaldTrump, and Donald Trump, while my graph focused on MAGA (Make America Great Again), TCOT (The Conservatives on Twitter), and NRA, with a smaller connection to Trump. One of the biggest surprises was the quantity of Russian tweets from a sample of my data. In one day, I had ~12.2% Russian tweets in my dataset, which brings up the question of if #2a has a different meaning in Russia. After further inspection, I noticed there was no difference, rather the Russian tweets were discussing the Russian military against the United States military. It was difficult to discuss the words per hour\/day since we had varying time differences between the graph. I focused on tweets per hour, while Tim and Jennifer focused on tweets per day.<\/p>\n
The biggest takeaway from my data was how most of the tweets are of a more conservative mindset. I was expecting to see an argument between two political views, yet my hashtag was heavily conservative. This could be due to my data being collected post-election and during a heavy conservative media dump. I feel as if the Wordsplash and SNA graph tell the best story of my data since they are easily linked to each other. The connection between my hashtag and the presidential cabinet is very clear when looking at these two sources, and I plan on showing the relationship between more specifically Jeff Sessions and the NRA.<\/p>\n
For the presentation, I am planning:<\/p>\n
Analyzing each graph against the group, the biggest difference I noticed was in the Gephi maps. Tim’s graph was very spread out with not many connections, while Jennifer’s was very connected and had some interesting groups such as the ACLU in hers. My graph had similar connections as Jennifer’s, but it was more group-focused. Each…<\/p>\n","protected":false},"author":1789,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[13],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3469"}],"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\/1789"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=3469"}],"version-history":[{"count":4,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3469\/revisions"}],"predecessor-version":[{"id":3566,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3469\/revisions\/3566"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=3469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=3469"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=3469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}