Analysis

These are the three main points that I cam covering in my analysis:

1.  I am exploring the negative effects of anonymity on the internet.

2. I am looking at different age groups in order to get a better idea of how negativity changes depending on age groups.

3. I was not surprised to see that the most negativity came from college aged students.

Formspring.com (Females ages 16-18)

The following graphs are for the data from the three junior females in a two week period:

Coding:

Positive: Any comment complimenting the user or using positive words

Example: "You looked hot in those prom pictures"

Negative: Any derogatory comment toward the user or any person

Example: "Ur face looks like it got hit by a baseball bat"

Neutral: Any comment asking a question that is neither negative or complimentary. 

Example: "What color is your car?"

The following graph is a total of all posts from the three different subjects in the two week period:

Formspring.com (Males ages 16-18)

The following graphs are for the data from the three junior males in a two week period:

Coding:

Positive: Any comment complimenting the user or using positive words

Example: "You looked hot in those prom pictures"

Negative: Any derogatory comment toward the user or any person

Example: "Ur face looks like it got hit by a baseball bat"

Neutral: Any comment asking a question that is neither negative or complimentary. 

Example: "What color is your car?"

The following graph is the average number of positive, negative, and neutral posts for the data set for males: 

The following graph is the average of all female's and male's positive, negative, and neutral posts:

From my archived data from Formspring.com, I saw three different trends.  The first trend is that in the female category, more questions were asked as a whole.  I realize that most of the questions were neutral, however this number is significantly higher than that of the male category.  Many of the questions asked to the females were about random topics.  Example: "What color do you want your dinosaur to be?"

The second trend I observed was that the males had significantly higher numbers in the category of positive posts than the females.  Rather than hateful messages or questions, the males were receiving more complimentary posts.  Example: "You looked so hot yesterday."

The third trend is that taking each sample individually, the neutral comments were most prevalent, followed by positive comments, and lastly followed by negative comments.  I think that the high number of neutral comments is due to what seems to be ongoing conversation between one poster and the user.  Example: 1st post: "Hey how are you?" Reply: "I'm good how are you?" 2nd post: "awesome we should hang out sometime."   I think that negative comments were the second most prevalent because of the anonymity of the setting.  The posters have nothing tying them to the negative post and therefore are more likely to post negatively.

CollegeACB.com (college aged subjects)

The following chart is for the original posts on collegeacb.com:

Coding:

Positive: Any post speaking positively about a group or person.

Example: "Joe Smith*...quite a nice guy"

Negative: Any post speaking negatively about a group or person.

Example: "Tri Delta* is weird: So i was just at tridelta* and i felt like i had to report how it was...bunch of creepy guys there haha i feel like tridelts* are all creepy"

Neutral: Any post asking a question.

Example: "Engineering School"

The following chart is for the replies on collegeacb.com:

Coding:

Positive: Any reply speaking positively to the original post or about someone.

Example: "Don't hate, she is a nice girl."

Negative: Any reply speaking negatively to the person who posted or the topic of the post.

Example: "The 'point' of them is for those kids who can't make friends normally so they have to pay a ridiculous amount of money to buy some friends."

Neutral: Any reply talking about a different topic or asking a question.

Example: "What about Kappa?"

By analyzing the data from CollegeACB.com, I was able to see that college students were significantly greater contributors to negative and hostile posts in an anonymous online setting.  Example: "She is so ugly." I hypothesis that this is because of the average age group of people attending college.  They too feel no attachment to the negativity in their posts because everything is anonymous.  There is also a large pool of people to "attack" via negative posts on this website.  The poster can choose any topic they want to write about in contrast to formspring where the posters are somewhat limited to the user they are writing to.  

Postsecret.com (unknown ages)

The following graph is for the data from postsecret.com in a two week period:

Coding:

Positive: Any post showing happiness or excitement.

Example: "THIS WEEKEND WAS MORE FUN THAN I THOUGHT WAS POSSIBLE IN ADULTHOOD."

Negative: Any derogatory post or a post that showed sadness or pain.

Example: "YOU ARE NOT A schizophrenic.  You've just done A FUCK TON of drugs.  Get it right."

Neutral: Any post that was neither positive or negative.

Example: "I am a journalist and I am commenting on my own articles."

Postsecret offers an online community for users of all ages.  I hypothesis that the number of negative posts was significantly lower because the site has such a wide variety of users.  Because we cannot specifically label each post with an age, it does not give us a good idea of of the negativity being posted in this anonymous online setting.