Lab 6 #Energy

Our clouds were similar except for mine because I no connections between the topics and different communities on teh gephi graph.  Everyone else, #keystone, #standinrock, and #climatechange all had connected graphs that look like the Les Mis example.  Everyone had connected graphs that would tell the story of the bigger conversation that was going on.  That people were really outraged by what is happening in the world.  Friends are all talking about these hashtags except energy either agreeing or disagreeing with each other.  The mapping tweets really didn’t tell any of us anything about our hashtags they were not a vital part for us to tell our stories.  The word cloud, gephi graph, and tweets per day charts are very telling of the story our hashtags are telling.

Langauge was pretty even across the board everyone had the majority english 88% or higher.  We also looked at the word clouds and my word cloud has no overlap with anyone else.  My other group mates have word overlap where I do not.  My data is continually different from my peers.  As Tyler pointed out that is really interesting because my hashtag is broader than that of my peers, which makes it fascinating that it does not encompass my environmental partners.  It is really interesting that my graph does not overlap at all when our group could be called the #energy group as well as the #environment group. It’s really interesting to see how my topic is the broadest and it looks nothing like my peers, I’m an out liar.

In terms of tweets per day graphs everyone graphs and tweet numbers were affected by something happening in the media that day or that week.  The media and news sparked a reaction in the twittersphere.  Especially for me in energy 60,000 of my 80,000 tweets occurred during an 11 day period all because Trump dumped the Obama green initiative.

For #energy I think my most telling maps are the tweets per day chart showing that Trump caused the spike in my total number of tweets.  Also I think looking at my gephi analysis could be telling of how different my data is from my environmental fellows.  I really want to use my word cloud because it shows what people are tweeting about and the big buzz words that surround #energy. I would use my word cloud and tweets per day charts to really show the story that energy is telling about what is going on in the world and what people are tweeting about.  But if I wanted to tell the story of how my hashtag is different and an out liar from my peers I would use my gephi graph and my word cloud to show I have no similarities to my peers in environment.  So right now I am leaning towards looking at the world cloud and tweets per day charts to really tell my #energy story.  It tells more about the data and what #energy really means in an environmental context. My data has been able to separate from #energy I ate a banana today and #energy need more solar panels.

 

 

Presentation Outline:

Title Slide:

#Energy By Jake Bennett

Slide 2: World Cloud

Word Cloud chart pictured (I will show the chart and talk about the big words and how they came up, how I had to find the different between #energy oil crisis and #energy I ate a banana today. )  The following slide will be the numbers break down of the chart. That I chose this chart because it tells the story of what people are tweeting about.

Slide 3: Stats and Facts from the Word Cloud Work of the filter bubble?

With the big buzz word numbers listed to the side.  I.E. how many tweets were analyzed for that cloud, (My word cloud has come up with energy 2392.  Power 197. Panels 129. Solar 370.  Those are my most popular words.  What is fantastic is that my tweets and words associated with #energy are in regards to clean renewable energy, and not about a banana gave me #energy.  That the part of #energy people are tweeting about is power and solar panels, as a new form of renewable energy. What is fascinating is renewable and oil have close to the same word count, (103 renewable, 104 oil).  It’s crazy how those words are almost polar opposites yet they surround #energy the same amount of times.  As I wrote above how Power panel and solar were some of my most frequented words.  That is great for my data because it means that tweets about #energy are about renewable energy. )

Slide 4: Tweets per day Chart

Show the 11 day period and how it is important and how it is a reflection of the media and that 60,000 of my 80,000 total tweets came in a 11 day period.

Slide 5:

Average:  5,056.5

Median: 6560

Mode: N/A (Everything occurred once)

Sum: 55,622 (total was 80,969 all semester, but for purpose to graphs and charting I focused on the 11 days above)

MAX: 6,621

MIN: 1,238

Range: 5,383

 

Side 6: Trump the reason for the spike filter bubble the answer?

Trump dropped the Obama green initiative act and through it out the window.  The Filter bubble under my google name created in class has become a massive proponent for green iniative and everything google with energy and my searches for articles and my word cloud filter were all #energy related words in the sense of clean renewable energy.  Define filter bubble how it’s personalizing the internet and I think it was personalized for me for this project, and how it would hide the data of #energy I just worked out.  Focused on the renewable energy aspect.

 

Citation Page:

Trump article about him dropping Obama’s plan

Filter bubble article