The first graphs we looked at were the maps which showed the mapped tweets. When comparing my maps to Will\u2019s and Jordan\u2019s, we realized that none of us gained much information from locating our tweets. We each agreed that we had too few mapped tweets, probably due to the privacy settings of users or the few number of tweets we scraped after the first week. The word clouds were the first visual representations that said a lot about our data. Will mentioned he was finally able to connect some terms in his word cloud to his hashtag and get an idea of the types of conversations that were happening. Moving onto the SNA, it was really the first time we each had significantly different looking graphs. Jordan\u2019s graph revealed the most central nodes, meaning the most conversations. We all agreed that looking up who these Tweeters were was helpful to understand why specific conversations were happening. Even though Will and I had less interconnected nodes, it still told us something about our data.<\/p>\n
We found it interesting that despite our different types of hashtags, the same graphs revealed the best information. My SNA graph help reveal what I expected to get from my hashtag, which were very spread out nodes with few connections. My word cloud and word graph helped emphasize the wide range of tweets the hashtag can be used in, showing me the varied meanings of it. It wasn\u2019t until the SNA and later labs that I began to think of my hashtag of having a very simple meaning. I realized that it is a term that a lot of people just slapped onto the end of tweets regardless of their topic. The lot of nodes that weren\u2019t connected to any others were confirmations of this idea.The graphs that tell the strongest story are the SNA, graph of tweets per day, and the word cloud. I chose to include the graph of tweets per day as a strong one because it had me look into what was going on each day. Even finding one day that seemed of some significance was valuable to my research. The word cloud told a strong story about the words most commonly associated with my hashtag. I was able to see that my hashtag was used with a fair number of social topics like the election or racial issues in America which gave me some relief. Lastly, the SNA graph tells a really strong story about the actual conversations around the hashtag. It shows the multi-faceted side of the word and how it is used all the time for the same reason, but for different topics. Each person who uses the term in a tweet does so for the same purpose, which is to express\u00a0a need for attention to a specific topic. I think the broadness of the term is shown by this graph, how it can be used individually or spark a conversation.<\/p>\n
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