This post is a continuation on multiple previous posts from fellow classmates. Many of them have suggested that social media accounts are a representation of themselves, offering to the public only the aspects of their lives that they consider positive. These presented selves are not the reality, and are just references to their “redeemable” qualities. Whether or not this is the case, I would argue that it is impossible to display every detail of your life on to a social media page. With that in mind, it makes sense to post only experiences and traits that are buoyant.
Earlier this week we discussed Big Data and possible definitions for the term. I believe Rei brought up that perhaps Big Data is something that is collective, as in not pertaining to the individual. I believe that this is true if you must be able to draw “on large data sets to identify patterns in order to make economic, social, technical, and legal claims.” (Boyd and Crawford 663) That is because we cannot feasibly document the individual to an extent that would be considered large data. There is so much information on an individual that they themselves may be unaware of. For me personally, I am sure that there are plenty of important aspects of my life that I have forgotten or was never aware of. I imagine that this value would be infinite. If it were possible to analyze each thought, interaction, and choice of a person, I believe that would be large a enough data set.
Matthew – this is a terrific extension of ideas in earlier posts this week. I’m persuaded by the claim here that it’s impossible to display every detail of one’s life in social media, that people are selective. I especially agree that we are not even aware of al the data we might generate. Subjectively, we are not even completely knowable to ourselves.
Further, the idea in this post that we should start with a collective definition of big data is important. By doing that, it doesn’t matter how large or small the data set is. Instead, we define it by the relationships among data points in the set, and relations across data sets. (And this is what boyd and Crawford propose in their piece.) This all makes “big data” a misnomer, or misguided, because it often involves measurements and quantities.