Among the Environment Group, there were some interesting observations we discovered. Our clouds were similar, except for #energy, which seemed to have few connecting conversations. There were very few singleton nodes who don’t have any edges, with most of the singleton nodes having at least a few edges. This likely means everyday regular people are generally tweeting about #climatechange, #keystoneXL, and #standingrock, but since they aren’t a big wig Twitter user with lots of followers, they have few edges, but at least their friends agree with them and are tweeting back and forth.
The tweet language pie charts were not surprisingly English-heavy, with mostly 88% or more being in English. For mine, a surprising language was Dutch, which only 0.32% of the world speaks, yet it accounted for 0.60% of the tweets. This suggests Dutch speaking countries (Netherlands) are talking a lot about climate change. The Netherlands is a world leader in sustainable energy.
Our tweets per day graphs had a similar trend: all the hashtags seemed to be closely tied to media coverage with their respective topics. For example, when President Trump got rid of the Green Initiative, #energy spiked drastically. All our tweets per day graphs were volatile, with media coverage being the “y-axis”, so to speak. Our word clouds were interesting because you would think our clouds would have shared words, but we could not find a single shared word, despite having a shared overarching topic (environment).
For my presentation, I plan to focus on the following:
- How media coverage/current events affects #climatechange chatter on Twitter
- Possibly make a day-by-day graph of all my data, and overlay it with events or popular articles that happened throughout those days
- Make more sense of my word cloud
[slideshare id=75546567&doc=climatechange-170430171556]
http://[slideshare id=75546567&doc=climatechange-170430171556]
It was interesting to see that everyone’s data was influenced by the media and what was happening that day, but that’s just kinda how twitter works. Was great to see those trends in your data and I love your presentation would love to see you really talk out that word cloud.
I expect you have some strong, clear ideas here–all of which I read in your previous labs–but I am not seeing here. Which media affected the cc discussion? About which topic? And how? Finally, which class readings will you bring into conversation with these arguments?
The tweets by day of all of your data should be stunning! Going back in and taking it to the next level is the best part!
I wonder if there is a way to include filter bubbles in your final project? Like to potentially see where people are contained to false assertions regarding climate chance vs. where people see reason? Just a thought! Very much looking forward to seeing this!