Group 4 – Page 2 – Data Culture

The Human Condition without the Humanity

In Augmented Memory, Digital Life, and Computers that Promise to Remember Everything, Isabel Peterson examines the implications of self-tracking to the point where you are able to recall everything that you see, hear, read write or feel. Her analysis suggests that this new form of the quantified self negates the fundamental purpose of human memory.

If we expect that human memory exists to store rather than generate reality, then humans are for information and not the other way round, a rhetoric that ultimately betrays posthuman conditions in these subtle assumptions.

Humanists would suggest that information is for humans to learn, use and wrestle with. What happens to our humanity when we exist and experience simply to recall? Is an emotion still an emotion when we only remember having it, not the way it actually felt? As this idea progresses, the functionality of the human brain diminishes and we approach a robotic society where we do rather than feel and analyze.

This begs the question that we asked at the end of class: do we need a computer to tell us how we feel? As one student asked, do I need my tinder app to tell me if I am having a good time on a date? I would argue that if we do not already know these things, then ultimately, how we feel does not matter. The readings highlight the argument that there is intrinsic value in the existence of the record for posterity. However, at this point the human condition is no longer worth reflecting on. What is the human condition without the humanity that is evident in our emotions, conflicts and questions?

February Class Attendance and Weather

Through tracking class attendance this week, collecting attendance from prior observer posts and looking up weather reports from this past month, I was able to create graphs that looked at the percentage of students present in class through the month of February with respect to the day’s weather and and high temperature.

There were 4 different descriptions of the weather on days we had classes. Clear, Scattered Clouds, Rain, or Snow. I averaged the percentage of students present on each type of weather days.

Time Between Question and Answer

On Tuesday and Thursday I observed the time between a question was finished being asked by Dr. Sample, and when a student began to give an answer. This data is below:

Tuesday times between question and answer (seconds): 3, 9, 6, 2, 8, 4, 2, 8, 11, 16, 5, 4, 6, 17, 9, 1

Tuesday average = 6.4 seconds

Thursday times between question and answer: 7, 6, 17, 5, 4, 11, 2, 13

Thursday average = 8.1 seconds.

Observations

Tuesday

Four students were wearing Bean boots

Three students brought coffee

Phones were used nine times while Dr. Sample was talking

Seven people were visibly wearing their misfits

Thursday

Six students were wearing Bean boots

One student brought coffee

Phones were used three times while Dr. Sample was talking

Eight people were visibly wearing their misfits

What Dr. Sample Said

My observing this week consisted of tracking the number of times Dr. Sample used some particular words, related to our overall idea of “Data Culture”, while he was talking to the class. I was thinking about also tracking frequently used words that people said for people who answered questions in class, but I forgot to count a couple on Tuesday’s class starting out, so I decided to abandon that idea. It was surprisingly easy to forget to count particular words that Dr. Sample spoke to the class so I may be off by one or two words, and also because a few words I started counting when Dr. Sample said them two or three times, though I think I did a good time of remembering how many he said before I started counting.

The particular words I chose to count were related to topics in our class, and specifically for the topics we talked about in class on Tuesday and Thursday. I wrote down some words before class that would be relevant to count, such as Data, Visualization, Information, Powerpoint, Maps, etc., but then added some more as I noticed Dr. Sample used them frequently in class. Here are the two tables for Tuesday and Thursday.

Tuesday’s class

Word	Number of times Dr. Sample said the specific word
Map(s)	75
Data	3
Visualization	6
Cartography/Cartographer	2
Information	6
Lie/Lying	8
Timelines	2
Graphically	1
Scale	8

Thursday’s class

Word	Number of times Dr. Sample said the specific word
Data	10
Network	3
Visualization	13
Information	9
Powerpoint	9
Timeline	3
Maps	11
Timechart	1
History	13

A couple of patterns stand out. First off, the number of times Dr. Sample said map or maps on Tuesday’s class was sizable compared to other words he said in class on both days. On Tuesday, we focused on maps for almost the entire class, and the map exercise where we had to draw maps from Studio D to Nummit, the Davidson Pizza Co, and Chipotle, was one part of class where Dr. Sample used “map(s)” frequently while speaking. “Visualization(s)” was a common word across both class, as well as “information”, which makes sense since they are relevant to our broad idea in class, talking about data. Also, Dr. Sample spoke the word “history” frequently in class on Thursday, since we spent a part of class on the Chronozoom where “history,” such as human history, featured prominently, and Powerpoint, because we read the article about Powerpoint and discussed Powerpoint towards the end of class.

Overall, counting particular words that Dr. Sample said in class is interesting because it can display the length of discussion we spent on topics for the most part. On Tuesday, almost the entire class we discussed maps, and therefore Dr. Sample used “map(s)” frequently. The words do not give any context behind the discussion, for example, words such as information, data, visualization(s), scale, etc., can be utilized in a variety of discussions, but they give a decent picture overall of the topic of discussion. Words were frequently used together with one another, such as “data visualization” or “data bars,” but I decided to keep the words separate to make the collection of data easier and more clear.

Focus as a Function of Time

In class today and Tuesday, I attempted to plot the overall class attention in relation to time, using a variety of indicators. Every 10 minutes, I recorded the number of students who were making eye contact with Dr. Sample, or with the student speaking, and the number of students making eye contact with either their computer or the class screen.

I quickly realized that these were poor groupings, with the potential to limit the data, as some of the readings discussed. I may have imposed my own demonstrations of attention on the entire class, who may have various learning styles. Further the data may be tainted due to both the non-traditional nature of the class, in which we are often working in groups or working alone, or if a person is staring into space in Dr. Sample’s direction, or staring at their phone. It further does not take into account people leaving, having their own conversations or falling asleep at their desks, which could be other indicators of attention in class.

That being said, it is clear from the graph from today’s class when we are working on activities involving screens, 1:50, 2:20 and 2:40. Focus also appears to be more inconsistent on Thursdays, as would be expected. It is more continuous, and decreases with time on Tuesday.

Does your location affect how much information is gathered on you?

While reading about the “cave dwellers” and the staggering amounts of information the Obama campaign generated on potential voters, I thought back to an article I read about swing states and how focused presidential campaigns are on them. This got me thinking about how people living in these states were likely targeted much more often than voters in other states that were almost guaranteed to go one way or the other.

The presidential candidates focused all of their public campaign event efforts into only 12 states during the 2012 election- why wouldn’t they focus all their data mining efforts to these states? This would give them less data do sift through and perhaps build even more detailed files on individuals as well as models predicting election results in those states.

Thinking about how geographically targeted political data mining is lead me to wonder about the effects geographic location has on your susceptibility to data mining as a whole. Are there certain areas whose residents see more information being gathered on them then others? Or is it a more arbitrary matter?

I would think that you would see a positive correlation between certain areas and the amount of information gathered on individuals in it, and that this correlation would have everything to do with the type of individuals who live in it. Population and other factors probably wouldn’t show much relation, but a very technologically savvy city like Champaign, IL would see more data mining efforts than a less connected city like Cheyenne, WY.

Perhaps there is no correlation at all and almost everyone is subject to big data mining, but I think it poses an interesting question.

Educating Digital Humanists

The digital humanities face a variety of challenges as Big Data becomes more essential to the field. One of these difficulties is the broad skill set that a digital humanist must acquire to organize and analyze these large data sets. Lev Manovich states in his chapter Trending: The Promises and the Challenges of Big Social Data, that most social and humanities researchers do not have the skills in computer science, statistics and data mining needed to take advantage of Big Data. There is a divide between those interested in the social implications of Big Data and those who can properly analyze it.

Typically when one thinks of a humanities or anthropology student, they do not associate computer science or statistics classes within their course schedule. A math or computer science student is typically not required to ask humanities questions in their studies. Although I know little about the humanities program at Davidson, I’ve found that there are two course sequences offered, The Western Tradition, and Cultures and Civilizations. These courses “encourage and reward clear thinking, speaking and writing.” Perhaps they could also benefit from computer science and data analysis skills that will be important for future social science discoveries.

From our conversations with Micki Kaufman, we learned she took a very non-traditional route to gain the skills see needed to become a digital humanist. Many of the skills she acquired over time through practice and experience with computer programs came not from her History studies at Columbia, but from the necessity to learn these skills so she could pursue her interests. The changing experiences of humanities with the advance of Big Data may require a rethinking of how students interested in these fields are educated.

Image URL: http://www.bc.edu/libraries/newsletter/2012summer/humanities/starter.jpg

Fitness Trackers and Insurance Companies

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When I was younger, I always wanted a trampoline. Each year for my birthday and Christmas, I would always write it on my list even though my parents had already explained that our insurance company wouldn’t allow it. Presumably, if they somehow found out we had a trampoline in our backyard, rates would skyrocket in preparation for the eminent injury that would occur as a result of its use. Fitness trackers may pose a similar threat. Though only touched on in a single paragraph by Marwic in “How Your Data Are Being Deeply Mined” fitness trackers may pose a future threat to buyers of health-insurance.

It is well known that previous or current health conditions are considered by health insurance companies before they prescribe a rate to consumers. Fitness trackers can observe and store data on diet, exercise, sleep, and several other factors that can all be predictors of a person’s general well being. Many trackers today also connect to applications that are in some way connected to the internet and social networks. This opens the door for data mining of one’s fitness data. This is a slippery slope as fitness data may be sold to insurance companies who may use it to make insurance rate decisions even in the absence of preexisting conditions. While I am unaware of any current application along these lines, I have no doubt that as our technology continues to develop, situations such as this will arise.

Online Personas

One point that Lev Manovich makes in his chapter titled Trending: The Promises and Challenges of Big Social Data that I found particularly interesting was his second objection to the “collapse of the deep data/surface-data divide” regarding the authenticity of interactions in social networks and the image presented by people online. He states that what people post and their participation online may not be an accurate or exact portrayal of their true personality and behavior. That may seem fairly obvious on the surface; however, when choosing to analyze and explore the vast amounts of data that can be accumulated through tweets, Facebook, Flickr, and Instagram photos, social network posts, etc., Manovich suggests that we bear in mind the data should be considered an “interface people present to the world,” not a clear view into people’s ideas, thoughts, and actions, and analyze the data accordingly.

The extent to which people curate their public persona online varies depending on the site, from a likely large extent on dating sites to a smaller extent on Twitter. But modifying our public personas online to such a large extent in some cases, I believe, mitigates one main purpose of social media, which is, according to Alice Marwick’s article, saving and exploring data about ourselves and others. Presenting fictional or exaggerated images or posts does not create the same level of discovery and connection between yourself and others on social media as your real images and posts do. Also, we may still have control over what we post, like, tweet, and so on, but companies and the government can still glean useful data from our cultivated online personas.

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