weathermap of feeling
With a collecting data at random for two weeks, no one would think that these random everyday occurrences, news headlines and pictures would be able to connect. Surprisingly, all of the data collection did have some connections. Each day seemed to have the same amount of fluctuation. The same amount of happy news and sad news, the same amount of happy images and sad images, happy blogs and sad blogs appeared from day to day. Although the stories were different a consistent amount was always apparent. For example, in the same day the headlines “Kodak Posts a Profit as Digital Revenue Nearly Triples” and “Bhutto heads Dubai; Blast Kills Eight” both occur and seem to balance the overall feeling of the day. This same amount of both positive and negative news each day seemed to be present on every level, from personal to national.
Although the same amount of happy data and the same amount of sad data were usually present each day, the amounts did not equal each other. Over the course of the two weeks I found it much more difficult to gather happy headlines and updates than sad ones. This is reflected in my project with much of the text being sad headlines and business updates. For instance, October 30th had four out of the five headlines about sad news. Many of the other days I had to search to find happy news in any category, including headlines, business and entertainment.
One part of the data collection that I did notice, however, was happy pictures appeared more often than sad ones. Many images went beyond being happy and were funny, like pictures of dogs dressed up in costumes, as Elvis and a pirate, which I found posted on dog blogs. Although there were sad images, the happy headlines, business updates or entertainment stories seemed to be more likely to have images to go along with them. For example, a new stamp with Yoda on it had a picture with the story as well as images of people carving pumpkins and a new type of “mushy” car. I think that overall my project showed that with the amount of text and images, each day is filled with many examples of both happy and sad emotions. The “weathermap” seems to vary slightly but the temperature and climate of the world around us seems to stay fairly constant from day to day.
For the comparison with other people’s weathermaps, I noticed a few things. Melissa, Melissa, Krystina and I all had similar headlines about large news stories like things happening in the environment, including rising gas prices and global warming. We also had similar stories about sports, especially baseball and other headlines about Oprah and the California fires. We thought that overall many stories were about authority and people around the country obeying authority. For example, there were two headlines about schools banning things, including peanut butter and Halloween costumes. The people did not seem to question the school banning these things and instead just accepted them as standards.
Overall, we agreed that there was a balance of emotions. Each day there were happy people and sad people, exciting news stories and depressing ones and the list continues. People tended to want to hear about the bad or depressing events going on across the nation rather than the good news. For instance, each day when we looked up entertainment, there was a story about Britney Spears and the fact that she is ruining her life and career. In general the weather was different across the country but usually correlated to people’s moods, which is to be expected. I think a way to improve the projects would be to pair up people who each viewed the assignment differently. For example, for my project I focused more on news stories, entertainment and business news. Other people however didn’t look at business or entertainment and paid attention to people’s blogs and personal emotion. I think that if people combined project it would result in a better weathermap for the whole country, that correlated people’s feelings with what was going on around them.
