#woke Final Presentation Slides & Video

The first graphs we looked at were the maps which showed the mapped tweets. When comparing my maps to Will’s and Jordan’s, 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’s 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.

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’t 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’t 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 a 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.

Presentation Outline:

• Begin by explaining the different perceptions/definitions of the term “woke” and why it is important. Also the different types of tweets I received.
• Key argument: Through analysis of data visualizations, #woke was found to be used in a wide range of tweets covering numerous topics. It’s a term that says a lot about the way people of all ages communicate and express opinion in today’s society.
• Show the word cloud, explain how the words are associated with #woke and what is says about it being a multi-faceted term.
• Show the SNA, explain Adore Delano’s tweet and the response it got. Also explain the reason for the separated nodes and what it says about how the tweet can/cannot connect people.
• Use Filter Bubble reading to explain how “social media affects the character of our society” and how #woke is an example of that.