pesaunders – Data Culture

Class Participation, Bracket Style

Since we’re in the middle of March Madness, I decided to track class participation in a bracket-style tournament format. A student advanced each time he or she directly addressed Dr. Sample in class discussion, or if Dr. Sample called on them to answer a question. One could only advance if there was an opponent available to defeat (therefore, someone who answered five straight questions would only win one matchup, not five). Although I could have played the role of the selection committee and “seeded” the field, I chose to use the list of names from the blogging groups page in the order they are listed.

The results from Tuesday’s class are below:

observations 3.20

Unfortunately, we ran out of time on Tuesday to determine a single winner. Also, the Gephi workshop on Thursday prevented the bracket format for serving as a good measure of participation (since we spent more time discussing in tables rather than as a whole class).

Although the bracket exercise was certainly amusing, it doesn’t appear to be the best way to determine the “best” participants; a lot of people who were bounced in the first round ended up speaking a lot later in the class, often more than the people who originally eliminated them. Perhaps the same critique could be applied to the NCAA basketball tournament as well.

Harness the Power of the #Hashtag

In the third chapter of his book Off the Network: Disrupting the Digital World, Ulises Ali Mejias describes how computers function together to form a network, defined as “a system of linked elements or nodes” (37). Digital networks, Mejias writes, use a combination of human and machine to assume social agency through social tagging systems. The concept may seem abstract, but for anyone who uses social media such as Twitter in their daily lives, tagging works as a beautifully simple way to sort through and classify information on a particular topic.

Perhaps what makes Twitter hashtags so appealing to its users is the fact that it operates as a “folksonomy” with few, if any, rules for what can and can’t be a hashtag. The relatively lawless nature of hashtags comes from the fact that “the negotiation of meaning during the process of classification is delegated from humans to code” (50). Mejias portrays this feature as “perhaps [the] greatest weakness” of social tagging systems, but I argue that any inefficiencies caused by the computer regulating the system are far outweighed by their benefits.

Yes, it is somewhat annoying that the same topic could take on multiple hashtags based on a misspelling or the choice to omit “the,” as in the example below.

Two people tweeting about the same thing won’t have their tweets sorted into the same “pile” because of their slightly different hashtag choice, but the inefficiency is minor and ultimately insignificant. What makes Twitter hashtags so fascinating is how they can provide a seamless link between celebrities and regular folks. When NASCAR driver Brad Keselowski—who has over 500,000 followers—wants to do an impromptu Q&A session with his fans, all he needs to do is harness the power of the hashtag, and computers handle the dirty work of aggregating all the submissions for him. In my view, it’s well worth a few small sacrifices in order to gain the “individual freedom and scale of access that only the internet can provide” (52).

Data about choices: Does it affect how we choose?

During Tuesday’s class, Dr. Sample played a video clip advertising the new Walking Dead video game, which was relevant because of how the game kept track of individual decisions and compiled statistics aggregating all of its users’ choices. Forced to make heat-of-the-moment decisions—“Do I save Ben, or let him fall to his death?”—users were likely to choose options they would later second-guess. The aggregated stats gave context for those split-second decisions, either comforting those in the majority (“I felt bad for letting him die, but at least 79% of users did the same thing”) or compounding regret for the few (“Wow, that really was stupid to try and save him”). Generally, people find comfort in choosing with the majority, although there are certainly rogues out there who would intentionally take the less-beaten path.

What interests me is the difference between how people choose when given the stats as opposed to when it’s simply a blind choice—particularly in the context of a NCAA tournament bracket. Every March, thousands of people join online bracket-picking tournaments, often relying on picking the favorites in each first-round matchup (especially for games involving schools no one’s ever heard of). I imagine that, rather than blindly guessing, most people would lean toward the team that has, say, 71% of users picking them, rather than the underdog with 29% on their side. What intrigues me about this is the possibility of a snowball effect, where people disproportionately favor the team with 52% support, which pushes the number higher to 53%, which makes users even more likely to pick them, and so on. And then there are the aforementioned rogues, who intentionally pick an underdog they know nothing about simply because of the thrill of contradicting the mainstream opinion. Once the data on users’ choices is available to the users themselves, decisions can quickly change—in video games, bracket pools, and surely other fields as well.

Image credit: Kawakami, Mark. “Using YUI 3 to Build the Yahoo! Sports Tourney Pick’em Game.” 19 Mar. 2010. Web. Accessed 18 Feb. 2015. <http://yuiblog.com/blog/2010/03/19/tourney-pickem/>

Speaking Up in Class: Time Log

class layout For Tuesday and Thursday’s classes, I logged when and from which table any student spoke up in class to answer a question, share a thought, etc. I arbitrarily assigned numbers to each table according to the diagram on the right.

The data appear below.

Tuesday 2/3/15		Thursday 2/5/15
time	table	time	table
1:42	3	1:42	8
1:44	3	2:10	4
1:44	5	2:22	1
1:45	2	2:25	3
1:46	8	2:32	8
1:46	3	2:33	7
1:53	3	2:34	7
1:54	5	2:35	3
1:55	5	2:39	8
2:04	4	2:43	1
2:05	1	2:44	8
2:07	4	2:52	2
2:08	3	2:53	7
2:09	3	2:54	7
2:10	8	2:55	8
2:10	2
2:10	6
2:10	7
2:11	5
2:12	8
2:13	4
2:28	7
2:30	6
2:30	3
2:32	7
2:32	4
2:33	8
2:34	2
2:36	4
2:36	3

Malleability of Data

In two weeks of Data Culture, we’ve wrestled with the meaning of “data,” debating whether data are merely fodder for larger arguments or potent enough to make a definitive argument on their own. David Lyon, in his 1994 book The Electronic Eye: The Rise of Surveillance Society, posits that electronic data are too malleable to simply be taken at face value.

Lyon employs the term “dataveillance” to describe the modern methods of surveillance, which take the form of big data and supercomputers, rendering the Big Brother-style video surveillance of Orwell’s Nineteen Eighty-Four unnecessary in the modern world. With extensive information on file about the demographics, histories, and consumer preferences of a population, governments and corporations both appear to have the details of the populace at their fingertips—a frightening thought, for sure. But Lyon counters that data’s malleability makes “confidence in the reliability of the record somewhat naïve” (59). For one, data stored by computers can easily be erased without being conspicuous in their absence. As a result, the “electronic trail” of data doesn’t necessarily tell its entire story. With audio and video surveillance, however, broken links are impossible to ignore—consider the missing eighteen minutes of the Watergate tapes. Additionally, Lyon brings up the issue of identity theft; I may not be able to impersonate my grandmother well enough to fool surveillance video, but I could certainly impersonate her while shopping online if given the right data.

The lesson, it seems, is that data can’t be fully trusted: not seeing means not (necessarily) believing. Therefore, it follows that “dataveillance” fails to embody Bentham’s original Panopticon structure, since the central authority has an unclear view of what its “inmates” (i.e. citizens or clientele) are truly up to. Those who fear that big data has ushered Big Brother into our daily lives ought to relax, because numbers only tell part of the story.

Lyon, David. “From Big Brother to the Electronic Panopticon.” The Electronic Eye: The Rise of Surveillance Society. Minneapolis: U of Minnesota, 1994. 57-79. Print.