Our group first looked at our gephi analysis, which was very similar for #climatechange, #standingrock, and #keystonexl, but appeared significantly different because in #energy, very few conversations occurred. We observed Tyler, Taylor, and my data had a lot of single nodes with at least a few edges, while Jake’s data had many nodes without any edges – meaning conversation was occurring but not between many twitter users.<\/p>\n
With respect to our language pie charts, we all had about 90% English text, which was expected.<\/p>\n
Mapping of tweets per day also revealed little about #climatechange, #standingrock, and #keystonexl, yet evidenced a huge spike in #energy, accounting for the majority of the data taken for this hashtag. Jake said he collected the overwhelming majority of his data over an 11 day period when President Trump repealed the Obama Climate Change Initiative. This honestly surprised me a lot considering how broad #energy seems. However, as we look at Jake’s gephi graph, it shows little amounts of large conversation occurring in #energy. Perhaps, #energy is too broad, lacking a consistent place in people’s tweets and also a propensity to surge on certain days (except when something cataclysmic happens like Trump’s repeal of climate change initiatives.)<\/p>\n
With respect to our text analysis, our group was surprised by how little our data overlapped. None of our major words were consistent, and my data seemed to map a single event at standing rock while my peers data seemed to highlight overt terms about their topics. This evidences the episodic nature of my data vs. the more fluid continuity of my peers data.<\/p>\n
I think my word cloud spoke the loudest of our graphs from lab 3, however, Taylor’s gephi graph and Jake’s showed opposite and very revealing data! Moreover, #energy seemed to have a more profound tweet per day graph. All the same, between the four of us, none of our topics lacked in dynamism.<\/p>\n
Title Slide – I will use a powerful image from standing rock<\/p>\n
Hashtag topic – unsure which media sources I will use, but I have a ton available.<\/p>\n
Key findings – TEXT ANALYSIS!!!! “history condensed into a single photo” – relate this to Sitting Bull\/history of standing rock\/American culture – also use the cold discussion to talk about liveliness vs. live data<\/p>\n
Key findings – tweets per day\/gephi cloud analysis – talk about the filter bubble\/software sorted technologies<\/p>\n
Summary\/Concl. – Standing rock data doesn’t necessarily tell us all we want to know about the movement, but it presents a dynamic variety of facets to how people are communicating with one another in the movement. The data reveals real time updates, public service announcements, and historical commentary alike, and in the context of understanding what the Standing Rock movement is, examining this data is imperative.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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