In terms of data privacy I probably have very bad browsing habits. I regularly use websites which use cookies and subscribe to emails and other offers from sites I frequent. I allow Google Chrome to save all of my passwords and information just to make giving my data to other companies more convenient for me. I guess I’m not particularly mindful of where my data is going just because nothing I do on my computer is so private that it would be a disaster if it were leaked. In order to market towards me companies would only need to look at my online purchases and frequently viewed items. It seems as if I do most of my shopping online now with all of the free 2 day shipping options and all of my buying habits are out in the open. It’s obvious to me now even that the ads I get on my browser are tailored towards me based off of my internet searches and browsing history. I can also see this being transferred to my social media account as now I am seeing adds tailored to what I would usually only see as banners. As bad as it is, I like having ads come up that I’m actually interested in as apposed to those I really would find it annoying to be in the way of my feed. Recently, I’ve even noticed that it’s not just my actions online that affect the ads being placed in front of me, but what I say as well. There have been too many times that ads have popped up for things that I specifically remember having conversations about, but would never be interested in buying.
Data Visualization Blog Post
This example of data visualization is a network map arranged from broad sets of data down to more specific categories. In this map the data being represented is one persons phone calls over the course of one year. It starts with only two main categories after the initial number of total calls, spam and trusted, before it gets very specific, who the actual caller was. The thickness of the lines shows the frequency if the caller, and the lines are easy to differentiate due to their difference in color. From all of this information I was able to learn that the vast majority of this person’s phone calls were spam calls, most of those unidentified. It seemed to be particularly useful to use this type of data visualization because of the sheer number of factors contributing to the data set; it is all arranged well enough that it doesn’t become confusing to look at. This would be particularly useful for people easily susceptible to scams. By analyzing and recognizing trends for scam phone calls, those people can be informed of the attributes and nature of scammers. I also find interest in the fact that this person has called their repair shop more times than their sister.
The data visualization above could only be described as a word cloud. It depicts words used in high frequency in tweets after India’s Supreme Court ruled out the criminalization of homosexuality. The bigger the word, the more times it was used in tweets. The format of this data visualization is important to the message being conveyed. With words like “love,” “LGBT,” and “India” having the most space in the image, a lot of attention is first drawn to them. Even smaller words still carry only positive messages which shows the campaign of the data. Certainly there must have been some hateful comments that were excluded form this research, so not the entire picture is shown. Besides that though, I learned of apparent overwhelming support of India’s Supreme Court decision, and I was able to learn it very quickly. There wasn’t a lot if thought or analyzing that had to go into this in order to interpret the results of the collected data. It’s interesting as to why they picked so many words the be green. There doesn’t seem to be any sort of pattern to the coloring of words, yet the grouping of green draw the eye to those 6 or 7 right by each other.