Data Culture – Page 7 – DIG 210 (Spring 2015)

Transforming Experience Into Statistics

Ann Helen Petersen , in her article “Big Mother Is Watching You”, describes the prominence that wearable tracking devices has gained, and the reasons behind this sudden change. She discusses the fact that companies are attempting to gather data about people in order to “improve” their lives. This improvement will be bought on by the fact that users wearing the devices will be able to analyze themselves in ways that they could not before. These people now have the power to dissect their own lives, and in a certain way have more control over their experiences. While I agree that the new wave of wearable technology does empower the user to a certain extent, I would like to propose the viewpoint that this technology is a two-sided sword.

The good comes from the empowerment. It is a great thing to be able to self test your urine to make sure that you are healthy, or to check up on your hear rate to make sure that you are not in danger of a heart attack. These are benefits that wearable technology provide. Our lives are made easier through the MimoBaby that allows the parents to maximize their child caring methods, while allowing the more tired parent to sleep more. It is beneficial to measure bodily food intakes and monitor weight among things, if one wishes to keep a healthy lifestyle. The purpose of wearable technology is good. The negative aspects come into play when one talks about the specific numbers themselves rather than their purposes.

Running

While people are able to monitor their lives and check all the statistics of their day, they begin to pay more attention to these numbers, than the actions that they are doing. In our high-tech society , many people are constantly on their phones, and data-tracking gives them more of a reason to check their screens. I believe that we as a society will get to a certain point where we no longer perform activity simply for the pleasure of it/because we want to, rather because we feel the need to live up to the numbers that we are being shown. It is a possibility that parents will feed their child simply because a machine is telling them to, not because the parent has developed a bond that allows them to predict baby needs. Some may run and have a great time running, but will not get the full experience if they are solely worried about the numbers of the run. These statistics have the power to put the user in a certain mindset where numbers are the only important thing. I do not wish to sound apocalyptic in any way, but it is imperative that we consider how full disclosure of our life experience will affect our overall life experience . We need uncertainty in our lives; that is how we learn best. If one knew exactly what was going to happen in one’s life, would it be as much fun? This question can be applied here. Petersen agrees that the ability to rely on ourselves for better life improvement makes us feel more alive than relying on an app. Wearable technology does have benefits, but we must not let statistics distract us from living.

Beware of the Wearable Devices

In her article “Big Mother Is Watching You,” Anne Helen Petersen discusses the recent rise in popularity of wearable data tracking devices. Petersen highlights devices that run the gamut in terms of functionality, ranging form the mundane fitness tracker to devices made for children to track their elderly parents’ household routines. Within the article, Petersen also hints at how her own fitness tracker use has affected, and potentially influenced her behavior. She notes that she has become obsessed with her sleeping data, and that she even purposefully excludes certain nights from her data collection, so as not to ruin her nightly averages.

Later in the article, Petersen discusses the potential effects that fitness trackers and other wearable devices may have on the insurance and medical fields. Specifically, she speculates that if wearable devices become more sophisticated, they may be able to help identify certain medical conditions, thereby reducing the number of trips to a physician’s office. Wearable health trackers, pending some technological innovations, have the potential to eliminate the knowledge divide between the general public and highly-educated doctors. While it may be years before these innovations are in place, they would certainly impact the insurance industry as an individual’s riskiness could be more accurately calculated using their accumulated data.

While purposefully altering one’s own sleep data and the potential health industry changes associated with fitness trackers may not seem related at first, there do exist some important interactions between the two. The first effect that comes to mind is the placebo effect that could exist simply from wearing a fitness tracker. It is likely that someone who is constantly reminded of their physical activity will workout more and generally be more health-conscious. While this encourages healthy behavior, it may occur even when the their fitness tracker is not working properly. For example, even when the R65 application was down, I found myself feeling guilty for everyday that I didn’t work out, despite the fact that any exercise I got wouldn’t be recording anyway. Additionally, health trackers pose a potentially adverse effect on the insurance industry. If customer’s data is being mined for insurance purposes, then someone who artificially alters their data has the ability make themselves appear as a less risky investment than they truly are. With the rise of fitness trackers and the overall quantified self movement gaining traction, the consequences of our daily data become increasingly more interesting and controversial. While “forgetting” to wear our bands when we go to sleep may seem harmless now, this could become a real problem if fitness trackers continue their rise in prevalence in our daily lives.

Posture and Class Attentiveness

This week, I observed how students’ posture and positioning affected their overall class participation. I looked at the correlation between characteristics, such as slouching forward/back and straight sitting to the amount of questions answered in class. I also collected data on whether the student was facing Dr. Sample and if they were cross legged. The data is as follows.

Total Amount of Questions Answered: 28

Slouched Forward: 13 (46.4%)

Slouched Back: 9 (32.1%)

Straight Sitting: 6 (21.4%)

________________________________________

Facing Professor: 12 (42.8%)

Cross Legged: 8 (28.5%)

Based on the data, it seems like students who were slouched forward as well as facing the professor were more likely to answer questions. In addition, I found it interesting that students who were slouched back were more likely to answer questions than those sitting up straight. I would guess that this is most likely because people usually do not practice perfect posture anywhere.

More Random Observations

From Class Thursday:

It seems plenty of people took advantage of the nice weather. I counted 11 people wearing shorts on Thursday and 5 wearing button down shirts, including Dr. Sample. At least 9 people were not, unfortunately, wearing their fitness bands; it’s undetermined how many of these absences were due to faulty equipment. Finally, Dr. Sample coughed an average of 1.27 times per minute during class over a 30 minute period.

Average Response Time in Class

In class on Thursday, I recorded the duration of every response to each question asked in class. I did this, because I thought it would be interesting to see if the response time increased as we got more in depth with a particular subject, and then when the subject changed, the response time would drop, and then slowly build back up again. The data indicates that this theory is not correct, because the response times do not appear to have any clearly visible trend. This is likely because as we go more in depth with a topic, the questions do not necessarily require an longer answer. In fact, some of the introductions to new topics might have required the longest answers, because it was necessary to describe a large amount of information to the class.

Summary Statistics (Time Spent Talking in Class)

This indicates that approximately 10 and 1/3 minutes of our class time was spent by students responding to questions.

Plot of Response Times

Plot of Difference in Response Times

Plot of Whether there was an Increase or Decrease in Length of Response (1=Increase)

Use Your Words

This week in class I chose to observe how often we use specific words. I chose the words “like”, “um”, “kinda”, “ok”, and “so”, because I wanted to see how often they actually occur in conversation. I’ve always been reprimanded for using “like” so often, so I wondered if it would be used as frequently in class as it is in normal conversation. Turns out, its used fairly commonly in class as well. Additionally, after the first hour of class I realized that almost every sentence either began with “so” or used “so” as a conjunction. Here are the results of my tally.

It turns out that “so” barely edged out “like” for most frequently used. “Um” was a decently close third place. “Ok” and “kinda” were not frequent at all, which I found surprising. In hindsight, other words that I would have been interesting to include are “you know”, “sorta”, “well”, “stuff”, and “whatever”.

Tracking the Fluidity of Conversations in Class

During Thursday’s class (3/12), I aimed to track the fluidity of conversations across different tables. Specifically, I wanted to see which tables participated most frequently and which tables most frequently interacted with each other. I listed the names of one person at each table to allow the viewer to orient the room. Additionally, I connected the lines at central nodes, when the conversation was shifting across tables. I define conversation as a continuous exchange between different tables. The single lines represent when only two tables talked, most often seen when Dr. Sample asked a question and a student responded. The longest conversation occurred when discussing the topic: What does connected mean to you?. There are some limitations to my observation: 1. I did not account for two people at the same table speaking consecutively 2. I did not track the time in which conversations took place. 3. There is a lot of “noise” because of the number of conversations shown and the size of the paper on which I tracked the conversations.

Hashtags and Graphs

This post will focus on the post by Peter Saunders about the hashtags. I absolutely agree with the point that hashtags sorted various information into certain categories and “handle the dirty work of aggregating” for the users. It is really interesting that Peter points out the inefficiency caused by the misspelling of hashtags. If you really think about how hashtags work, you will keywords, or tags, used in some blog websites including WordPress work the exactly same way. If you put the network into graphs, you might be able to see tags as endpoints that link to the endpoints on the other side, which are the posts. It resonates with the other reading, “Graphs” from Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg.

For instance, on instagram, you post a photo with #dataculture. The hashtag serves as a hyperlink between the category and the photo and thus the photo is the endpoint. The other way around, if you search the hashtag #dataculture, the hashtag becomes an endpoint with leads to the category. Just like the graph shown below:

If you have multiple of photos with multiple hashtags, the graph will go more complicated. And then with a large user group, the graph goes even crazier. This is where Cloud technology came in.

To draw a conclusion for this response, the hashtags are certainly inefficient sometimes because of the misspelling, but, instead of creating different databases, the hashtags create multiple hyperlinks to sort information into categories. Comparatively, hashtags are very efficient.

Graphs credit to:

http://en.wikipedia.org/wiki/Planar_graph

http://en.wikiversity.org/wiki/Web_Science/Part2:_Emerging_Web_Properties/Emerging_Structure_of_the_Web

http://en.wikipedia.org/wiki/Linked_data

Response

Going through the readers responses to the works, one thing that I was surprised that didn’t come up was the Bacon number. Although one of the readers did mention nodes and connections, nobody talked about the factor that Kevin Bacon is within eight friend layers of every single actor in every single film that the study could find. This is ridiculous when you think of the human race as a network because it shows how close we are truly interconnected.

Another reader wrote about how artificial intelligence is becoming more increasingly life life, there are still short comings. It seems as if my generation is obsessed with imagining a future without those short comings. We see newly released movies like the new Robocop or Chappie. We have corporations like IBM creating some of the most complex AI ever in Watson, the all time leader in Jeopardy. As of today we are only scratching the surface of AI’s, but eventually they will grow into the backbone of our industries, our households, and our lives. One can only speculate about what the future holds for us as a the Human network collides with artificial networks.

Hashtags and Tagging

This response will pay particular attention to the blog post “Harnessing the Power of #Hashtag”. This post was a very interesting and straightforward response to Mejia’s chapter on “Computers as Socializing Tools”. This post focusses on the implications of Hashtags in modern social media, paying particular attention to Twitter. This post describes various benefits that hashtags offer social media users.

I really enjoyed how the post divulged into how hashtags served as a “beautifully simple way to sort through and classify information on a particular topic”. I strongly agree with this, as hashtags truly make information on a certain topic easily accessible to everyone, as they organize the information into particular categories. Also, I agree that the small percentage of mistypes in hashtags are a small price to pay for all of the benefits that tagging can create. In my opinion, when most people use hashtags, they are careful to use the exact wording as the original hashtag.

The argument that hashtags help to bridge the gap between celebrity and “regular folks”is another intriguing point. Although this is true that hashtags enable everyone to have their tweets or posts categorized in the same place, it is very hard for a hashtag about a non-celebrity function to get much traction. So in my eyes, hashtags do not strongly help the celebrity/regular folk divide.

Also, I believe that photo tagging is a very important topic in the field of social media information exchange. The ability to make oneself known and tagged in someone else’s photo is a very helpful resource enabling people to widen their net of people who can view their photos.

The world of tagging and hashtags truly do serve as simple and efficient resources that enable information to be effectively categorized and spread.

http://media02.hongkiat.com/quicktips/fb-tagging.jpg