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.
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.
For the presentation, I am planning:
- demonstrate connection between presidential cabinet and NRA relating to my hashtag
- narrow down the connection to Attorney General Jeff Sessions & NRA
- discuss how twitter helped Jeff Sessions nomination
- demonstrate Session’s strong view on gun rights
- find related articles from the time period to help reinforce this discovery
Its been a pleasure serving with you this semester my friend. Thanks for another great post. Live long and prosper. I too noticed the same thing with our SNS analyses!
Harrison, I agree on your main argument of your hashtag! First, I am wondering if you could effectively show that Jeff Session’s nomination greatly influenced the popularity of your hashtag by using your word graph. I don’t think your word graph showed any words related to Jeff Sessions. You should perhaps consider replacing it with your tweets per time/day graph and discuss when media talked about Jeff Sessions more, how your tweets spiked. Second, you focus a lot on Jeff Sessions – I think that there is more to your data than just Jeff Sessions though. You should briefly talk about other things that I found interesting about your data including the amount of conservative accounts that you discovered (contrary to the literature about how “liberal” and “urban” Twitter users are) and the dominance of Russian tweets for a relatively domestic topic. You should maybe think more about what graphs that you should use to convey your main argument.
Sounds good–but the comments from your peers should not be overlooked! You did have more powerful findings! What article will help you support these arguments?