Each graph revealed several things when compared as a group. When looking at the SNA graph all three of us had only a few points on our maps. I thought this was interesting because we all had such large quantities of data and therefore expected there to be a significant amount of points on the map. In relation to the word cloud I found that Jack and Ian had very interesting word clouds. For instance Jack’s word cloud included words that caught my attention such as fascist, burn and women. There were also several words that overlapped in their clouds. My word cloud had more words in common with Ian’s cloud. The reason I think this occured\u00a0is because Syria is one of the countries that was banned by the Trump. The third data visualization that we completed was gephi graph. This was my personal favorite as I was interested to see how all my data connected together. In comparison to Jack and Ian’s graphs mine looked more like Ian’s as we both had a similar amount of connections. When it came to looking at the pie chart Jack and I had some significant differences. For example only 75% of his tweets were tweeted in english compared to my 89%. Jack also had came across about 14 more languages when looking at his tweets. When it came to comparing our number of tweets per day Jack looked at a greater amount of days, therefore his data showed more discrepancies.<\/p>\n
When looking at my data I believe that my gephi graph tells the strongest story in relation to my hashtag. I believe this to be true because I was able to see how the people of the world were talking about #muslimban. After analyzing my graph I was able to come to the conclusion that more people are against Trump’s policy than for it. I think this demonstrates\u00a0that more people are liberal when it comes to tweeting about #muslimban. The biggest point I would like to stress in relation to my graph is that the biggest node was the realdonaldtrump rather than POTUS. I believe this shows that many people are still ignoring the fact that the President of the United States is in fact Donald Trump. I also think this graph told the best story as it showed people were still tweeting about the muslim ban even after a month of it being implemented. Another point I would like to stress is that a significant number of tweets weren’t connected. I think this occurred based on the fact that my data was so large and there are so many different opinions when it comes to the Muslimban. The second data visualization that I felt told a strong story was my word cloud. All the words on the graph were able to sum up the most significant points in relation to my hashtag. For example nobannowall and theresistence were both frequently used in my tweets and are both phrases against Trump’s policies.<\/p>\n
Outline:<\/p>\n
Title Slide: #muslimban<\/p>\n
Background: Introduce what the Muslim Ban is\/ When it was implemented\/ how people reacted<\/p>\n
Data Visualization #1: Gephi graph: significance of the nodes<\/p>\n
Data Visualization #2: Word Cloud<\/p>\n
Readings: Relate to readings from class<\/p>\n
Summary: Sum up key findings\/ Argument\/ Citations<\/p>\n","protected":false},"excerpt":{"rendered":"
Each graph revealed several things when compared as a group. When looking at the SNA graph all three of us had only a few points on our maps. I thought this was interesting because we all had such large quantities of data and therefore expected there to be a significant amount of points on the…<\/p>\n","protected":false},"author":1966,"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\/3446"}],"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\/1966"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=3446"}],"version-history":[{"count":4,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3446\/revisions"}],"predecessor-version":[{"id":3555,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/3446\/revisions\/3555"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=3446"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=3446"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=3446"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}