Lab 2: Analyzing #SOTU2014 DH Project

This week I decided to focus on a particular Digital Humanities project that tracked twitter activity during the 2014 State of the Union Address, called #SOTU2014. This project is a website that displays the amount of tweets, topic of tweets, and dispersion of tweets (across the U.S.) at different times throughout the State of the Union Address. I thought this display was an interesting way to break down the public’s reaction to what was said in that speech. 

I started off by understanding where the information came from, which seems pretty accessible considering their data consists of twitter hashtags. From there, it was interesting to see how they organized the tweets in order to then present them. They categorized tweets by popular hashtags, with some of the standouts being “#jobs, #healthcare, #fairness.” I thought this was an interesting choice on how to organize the data. Something about the choice to follow hashtags made the information feel more relatable and easier to digest because it is something I see often. I also thought it was interesting that they had many pillars that they organized the data by, including the number of tweets that included each hashtag, the time they were tweeted, and also the state they were tweeted from. 

The way the data was organized was also very well presented, in my opinion. I especially liked the way the hashtag data was presented at the top of the page. I thought the colors and the way the graph was made to look artistic was easier to digest as an audience member. I would say that I am a visual learner, and it sparked more of my curiosity to look at the visual presentation and see that #fairness started to blow up at around time 20:45 in the speech and then remained an active component of the conversation. Beyond just being a pleasing visual representation, I liked how there was an option to click on a certain time on the model of hashtags that would take you to the section of the transcript that it directly reflected. This was a neat tool that allowed me to really explore the topic deeper than I would have otherwise. In the picture below, I wanted to show what it looks like when you click on a section of the data that is visualized at the top of the page. Notice how the associated section of the speech is highlighted in yellow!

As I mentioned above, I thought it was really creative that they used data (tweets) that are so broadly accessible, and in fact often widely accessed. Many people were active on twitter the day these tweets were sent out. I think that makes this project special because they took accessible data and analyzed it on a larger scale in order to show people the bigger picture they might not have seen otherwise. I think it was especially powerful because they put this information out without really making a staunch argument. It seemed more like they put the information out there in a more educational sense so that people could better understand the trends in reactions of the public.