To gather data for my Weather Map of Feelings, I looked at various blog entries containing the words “happy” and “sad” in the morning and the evening every day for two weeks. I also looked at headlines from the New York Times and other news sources, checked the weather, watched the news, and evaluated my own feelings. I choose to focus on the news stories that were either “e-mailed the most, blogged about the most, or read the most,” according to the statistics on the websites.
John Donne once said, “No man is an island,” and while gathering data for my Weather Map of Feelings I found this to be the case. I noticed that when I read many of the blog entries with the words “happy” or “sad” in them people were either, happy for someone, or happy because of someone, sad for someone, or sad because of someone. I found that this occurred much more often than people being happy or sad because of something. I found this observation to be very interesting for a few reasons. Firstly, I find that most people keeping blogs are in there mid-to-late teenage years or in their early-to-mid 20s. I think at these particular ages people are the most concerned about their place in the world, where they fit in relation to others, and feel perhaps more closely connected to the people around them. I noticed that the headlines of the day did not necessarily have any correlation to the feelings in blogs. If there was a particular day with more positive headlines it didn’t mean that there were more people writing happy thoughts in their blogs. I think this is because people are most highly affected by the things that are closest to them, while many of the sad headlines focused on the tragic wildfires in California, the blogs that I read happened to be from various other parts of the United States. The events that were happening in California probably did not carry enough wait with them to affect their overall state of emotion being through the day. However if something unpleasant happened to them or near them then they would mention that it, in fact, did have some sort of effect on their level of happiness.
When taking note of the news stories that were, read most, blogged about most often, and e-mailed to others most, I found them to be stories that were either, most alarming, most scandalous, or most tragic. For the most part, stories that were “pleasant,” did not seem to interest the general public as much as other these other types of news stories. I feel that since Americans seem to focus on these news stories they have become increasingly anxious. In the past few years America has experienced great tragedy, a terrorist attack, natural disasters such as hurricanes, flooding, and fires, and war. We are also beginning to fear many problems such as global warming, an energy crisis, and obesity. When reading blogs dealing with “happiness,” I found quite a few posts where people feel anxiety while happy. They felt that something bad was bound to happy. I think these events have caused an overall anxious and foreboding attitude in Americans that maybe didn’t exist a little over a decade ago.
I found it much easier to gather data in the evening than in the morning. People tend to write in the blogs in the evening more often and report on how their day turned out. I think this is because people wake up in the morning ready to start the day fresh. Perhaps they wake up in a certain mood, but throughout their day their mood changed, most were inclined to write about what during the day affected their mood rather than how they felt when they woke up in the morning, possibly because they could not explain why they were feeling this way. However I also noticed that in the morning the weather has more of an impact on peoples’ feelings than it does later on in the day. This is probably because when waking up in the morning, the first thing people notice is the lighting in their room and perhaps the weather. I know that on days when I woke up to clouds and rain, I felt less happy than on days when I woke up to sunshine.
After comparing my data with Melissa G, Jennifer, and Krystina, I found that we each had similar news headlines, and similar images gathered from news stories and in some cases we even had the exact same ones. Many of the news stories focused on the World Series, the California wild fires, the writer’s strike, and ways to be “green.” I think this shows what Americans are mainly concerned about and connected by. Americans will always be connected by sports especially baseball and football, we will always come together in times of disaster, and we are a democracy and will always discuss ideas and opinions on how to make the world better.
Melissa and I also discussed our theory that people who write blogs aren’t necessarily the same people who follow the news. For instance, many young people are not as active in following the news, while many older people do not keep blogs. Perhaps if the same people were reading the news and writing the blogs there would be more of a correlation between the overall emotions in news stories and the emotions in the blogs. We agreed that for some reason it seemed that people were generally more sad than happy, or at least seemed to vocalize their sadness more often.
After completing my observations I began to think of ways this project could have been more successful. I think it would be very interesting to create a weather map of feeling for Marist College. Instead of just reading blogs we could actually speak to other students on campus about their feelings and what caused these feelings on any given day. This would be helpful because then we could connect these feelings to actual events that happened around campus and in the surrounding areas. Of course these people on campus will ultimately be affected by other things, such as situations going on at home, personal experiences, and other things, but I think localizing the project and making it more concentrated would improve the accuracy of the results and give the results more value.
Overall, I think this was my favorite project because it was one where I could actually try to make connections between the data.