I expect to find most people tweeting about #climatechange in the Northern hemisphere, 0° to 90°latitude, since that is where English is more prevalent, and because I feel as though people in this area are more aware of climate change. I also expect the Western hemisphere, -90° to -180°longitude to have more tweets about #climatechange, however this does leave out most of Europe and Australia, two places where I expect to find a lot of tweets. I think my hometown, Boston, 44°, -70°. I expect some people to be tweeting from this area, but not many just because this is such a world issue, literally.
I have 69,923 tweets to date, with only 60 of them being mappable, which is 0.09%.
Yau writes about how data shapes these data visualizations, and that the visualizations are meant to communicate data to meaningful conclusions. My data might underrepresent non-English speakers, since I am only searching for #climatechange, which is obviously an English word/phrase. People in, say, Brazil may be talking about climate change on Twitter, but it will not be included in my data unless they use specifically #climatechange. Most of my geolocation tweets seem to be in the Northern hemisphere, as I expected, and a lot of tweets in the Western hemisphere. So I expect most of my tweets to be in the Northwestern quadrant, AKA mostly North America, which is mostly English-speaking. Upon closer look, most of the tweets I found are in English, which is no surprise. I expected a bit more tweets to be in more popular languages, such as French or Italian, and a surprising number of tweets in Thai…? Now that I look closer at the Thai tweets, they are all retweets of the same tweet. The fact that most of the tweets are in English again over-represents English speakers and underrepresents non-English speakers.
Based on how boyd & Crawford define Big Data, I do not feel as though I have Big Data. My data, while rich, should not be considered Big Data since my numbers do not speak for themselves, which is a concept boyd & Crawford present. Even the simple fact that I am scraping data from Twitter is not representative of “all people,” as “…it is an error to assume ‘people’ and ‘Twitter users’ are synonymous: they are a very particular sub-set,” (boyd & Crawford, 669). Additionally, of my collected over the past two weeks, only 0.09% of that data is mappable, so it is certainly not Big Data.
#Energy had more tweets than #climatechange because #energy is a common term and can explain a lot more than just environmental issues, such as working out or supplements. #KeystoneXL, not surprisingly, had far fewer tweets, since it is a specific topic, far more specific than #climatechange, which is a global issue.
CartoDB must have changed a lot from last year, so I don’t see any of these markers or paintbrushes or simple maps…
I mapped out the 18-24 age category in the 1980, 1990, 2000, and 2010 census in San Fransisco, a city where a lot of tweets about #climatechange were coming from. I chose this age group because as of now, when it comes to climate change, the Millennials (also known as Generation K) are the generation who will have to deal with climate change, and the one who can combat it, which is why I expect them to be the ones talking about climate change most on Twitter. What I found for San Fransisco, though, is that over the years, the distribution of 18-24 year olds has remained relatively uniform from 1980-2010, however, there seems to be a lot of young people in this city.
I read two articles on climate change that had unusual linkages. One was on how climate change and birth weights may be correlated. The article looked to examine “…the effect of extreme temperatures on birth weight, by race,” (Deschênes 212). The article did not examine anything on age, per se, but I’m guessing if a baby’s birth was affected by climate change (and realized by more people), then more people would be talking about climate change. Another article, “Climate Change and Human Evolution” focuses on just that – how climate change affects human evolution. Similar to the first article, if climate change has an effect on humans and their development, then surely more people will be talking about it, on platforms such as Twitter, using #climatechange.
Graham says software-sorted geographies means people’s decisions are more based on computers than we think. Going back to the two sources I found and how I am trying to track who is talking about climate change and where, I realize the more people realize how severe climate change is as a world epidemic, the more people will talk about it and share their views on the Internet. This, in turn, shows how people’s decisions and opinions can be easily shaped based off of where they gather their information, especially on the Internet, without even realizing it.