My initial interest in #NoDAPL came from my perception of the hashtag as a multi-issue conversation. The Dakota Access Pipeline, the focus o

All of my labs provided me the opportunity to view the same data differently, each visualization offered a fresh new look. Some were definitely more h

The graphs from the labs each have their own place of importance. Together, they show everything about the conversation regarding #alternativefacts. I

When comparing all the graphs as a group, you realize that each graph tells its own story! For example the map reveals where people are tweeting about

Our clouds were similar except for mine because I no connections between the topics and different communities on teh gephi graph.  Everyone else, #ke

In coming together as the Environmental group, it was interesting to see how little our data analysis overlapped in summary. When looking into the tex

[gview file="http://commons.trincoll.edu/amst-data-driven/files/2017/04/transgender-Final-Presentation.pptx"]Each of our graphs showed more or less th

Our group first looked at our gephi analysis, which was very similar for #climatechange, #standingrock, and #keystonexl, but appeared significantly di

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 connecti

Individually, each graph says something different about the data. The map shows a majority of the tweets concentrated in the U.S and the data corresp