When comparing all the graphs as a group, you realize that each graph tells its own story! For example the map reveals where people are tweeting about Obamacare around the world and reveal where this hashtag is most talked about. My map revealed that Obamacare was only tweeted about in the United States; no other location outside the United States had a geolocation, indicating that this is primarily an issue in the United States and only affects people in the United States. The word cloud also revealed its own story in a very unique way. The word cloud displays the most frequently used words in tweets about Obamacare and also showed different hashtags that are connected to Obamacare. I also realized that every word in the word cloud was in English, which meant that a majority of the tweets using #Obamacare were probably tweeted out of the United States, which is supported by my map. The two largest nodes in my SNA were ‘POTUS’ and ‘Speakerryan’, which both appear as two of the largest words in my word cloud as well. This shows that a majority of tweets referenced the President of the United States, Donald Trump, or the Speaker of the House, Paul Ryan and interestingly enough, both the President and Speaker of the House support repealing Obamacare. Unfortunately, I did not find my SNA to be too useful as the words came out very small and it is currently very hard to analyze unless I redo the SNA. The piechart I created did not reveal anything I did not already expect. It showed that a majority of the tweets regarding Obamacare were in English, with a total of 96% of tweets in English. 1% of my tweets were in German and 3% were in ‘Other languages’. This connects to my map and word cloud because Obamacare is an issue that primarily affects the people of the United States, so there is no surprise that my map showed geolocations only in the United States, no surprise that my word cloud only had words in English, and no surprise that 96% of my tweets were in English. I thought that the word graph did not reveal much about Obamacare and I don’t think it will be beneficial to have in my presentation.<\/p>\n
I think that the word cloud, social network analysis, map, \u00a0and pie chart all revealed that #Obamacare is primarily a topic that affects people in the United States, and that a majority of people who are tweeting about this are from the United States. The pie chart revealed that 96% of my tweets were in english, while the map indicated that tweets were only coming out of the United States. On top of that, the world cloud had words that were only in English, and that also revealed a variety of other topics\/hashtags that are related to Obamacare. The two visualizations that I think will be most beneficial to my topic and presentation are the word cloud, as well as the map of geolocations. I think these both support each other pretty well, and show how Obamacare and the huge issue with it only affects people in the United States, which is why we are seeing a majority of tweets coming from the United States and only English words in the word cloud.<\/p>\n
Outline:<\/p>\n
Slide 1: Title slide<\/p>\n
Slide 2: Background info on Obamacare<\/p>\n
Slide 3: Current issue with Obamacare<\/p>\n
Slide 4: Map of geolocations<\/p>\n
Slide 5: Word cloud<\/p>\n
Slide 6: relating these visualizations to readings from class<\/p>\n
Slide 7: conclusion slide<\/p>\n
Slide 8: works cited<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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