Final Presentation Prep – How does data define #KeystoneXL ?

In coming together as the Environmental group, it was interesting to see how little our data analysis overlapped in summary. When looking into the text analysis, there was basically little to none overlap to illustrate how parts of our own hashtag issues are interrelated. It was surprising to see that #Energy’s word cloud, while being probably the most broad hashtag out of the four of us, is still vastly different than all of our data. Additionally, even in comparing #KeystoneXL and #StandingRock, there was little overlap, which is surprising since you’d think they are pretty intertwined and related hashtag topics. Our gephi clouds were all pretty similar, unfortunately though #Energy was drastically different since the broad range of topics that go underneath the header of #Energy are all shown as completely separate conversations with no interactions between them on different levels. In looking at our graphs of language, across the board English was used 80% or more of the time, which either speaks to the fact that a majority of english-speakers care about energy and climate or that there is little use of Twitter in other countries. The graphs looking at tweets per day show that #Energy was heavily influenced by events that were occurring and the public reaction to such, similar to the data collected per day for #KeystoneXL. However, #StandingRock and #ClimateChange found more of a steady income of tweets per day, still amplified by the events that were occurring in that day but not in such a drastic way.

When looking through all the data analysis completed over the semester for #KeystoneXL, the word cloud and gephi cloud both illustrate #KeystoneXL as a wildly complicated and varied topic that incorporates many different perspectives and figures into the issue. This is interesting because these specific graphs allow you to see the total impact of the Keystone XL project and the many different directions that can diverge from its general discussion. When assessing the language pie chart, the Keystone XL issue is narrowed down to a specific population related to the hashtag, although I don’t quite believe it encompasses the issue entirely or its breadth of who it affects/is concerned about it. I think the graph depicting the tweets per day really does a good job of explaining how circumstantial my data was given the media being produced and the public reaction. Unfortunately, the map of geo locations was not of my data so I can’t say much on how it really adds to my hashtag or reveals the focus or meaning of the issue entirely. What I did appreciate from that analysis however was being able to really visualize how a different hashtag, while related to #KeystoneXL, could overlap with my data and being able to visualize #keystoneXL in the context of #Energy’s data. Thus, I think the geolocation map, the gephi cloud, and graph of tweets per day are the most valuable to characterize Keystone XL within the greater environmental context and they depict my hashtag as a complex issue that diverges into many different conversations and perspectives to portray the issue into different meanings.

What to focus on for final presentation:

• What is the Keystone XL project? Why is this a topic of debate now? What are its origins/previous media history and how does that make it a notable topic to research now?
• Show the map of geolocation data from #Energy next to where the Keystone pipeline is now and where it is projected to run. Then show similarities/differences in these maps. Also, talk about the maps of unemployment in areas where the pipeline exists over time and talk about expectations for unemployment of where it will be built. How does that correlate to the expectations of employment based on the extension of this pipeline. What does this say about the data you collected, the issue as a whole, and the public opinion of the pipeline extension?
• Show the gephi cloud and explain the many different conversations that occur stemming from #KeystoneXL, how they interrelate and what that says constitutes the topic of Keystone XL. Who is involved in the gephi cloud and how that describes the players of the issue and how large or small is the audience that rallies behind those specific players? So, who cares about #KeystoneXL?
• So in conclusion, based on data, what is Keystone XL? Is the meaning of Keystone XL different in the context of twitter data versus the meaning we learn?