Category: Group 1

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).

In Mejias’ chapter on “Computers as Socializing Tools” he sites the critique of social computing, a field focused on the modeling of social behavior, that in some way it simplifies human behavior by “dumbing it down” to a level which a computer can understand it. The creation of artificial intelligence or games like the Sims demonstrate attempts to recreate human life in the digital realm. These attempts so far have fallen short in recreating a 100 percent realistic picture. Therefore, the question remains can computerized algorithms, however complex, ever truly model how we behave? The answer depends who you ask. Critiques such as those mentioned by Mejias might challenge the plausibility. However, some physicists may argue otherwise. Theoretically, if a unified theory of physics such as string theory were ever to be proven, any concept of free will would be nullified. This would mean our brains operate no differently than computers, collecting stimuli and working through algorithms to arrive at an appropriate action. If this is truly the case, then there is no reason computers could not accurately model human behavior. The only obstacle to this goal would be understanding the complex algorithms of the human mind, which is certainly a daunting task.

David Easley and Jon Kleinberg’s chapter on graphs provides an instructional background on the mechanics of visual aids. Their explanations on distances between nodes, paths, and connectivity, however, read as overly specific. Developing “basic network properties in a unifying language” (23)  for graphs sounds unnecessary to a common reader because the very point of graphs is to make information accessible without the need for complicated directions. Visuals are so prevalent in our daily life that directions are unnecessary. Even seemingly private information made available to us with visual aids, like our Facebook data, is immediately digestible. Reconsidering how graphs are established, however, allows us to find similarities between visual aids that may be unexpected.

Through their discussion on social media, Easley and Kleinberg hinge upon a link between visual maps and connections with friends online. They introduce a concept named the “small-world phenomenon— the idea that the world looks “small” when you think of how short a path of friends it takes to get from you to almost anyone else” (35). The Guardian’s article on internet privacy opened our eyes to how closely linked humans are- even people who don’t know each other. I would argue that other graphs also work to shrink distant locations, and apply the idea of social network analysis to physical geography. Easley and Kleinberg use airline routes and subway maps to illustrate the graph’s ability to document “direct connections.” Shrinking one-hundred city blocks into twenty orderly dots makes an urban environment look compact. I can travel across an entire city by making a few stops along this lined? A flight from Atlanta to Tokyo, a fifteen hour flight across an entire ocean and continent, can be reduced to a two-page spread in a magazine. That journey looks pretty simple. As previously mentioned, graphs are incredibly accessible and benefit from not needing the mechanical directions provided by Easley and Kleinberg in their chapter. One thing graphs lack, however, is a sense of context. While they may be easy to read, their simplicity may imprint “the small-world phenomenon” on geography as well as people. Accessibility comes with a cost; whether or not such a cost is positive or negative is up for debate.

Through technology, we choose who we associate ourselves with. The association with someone would create an edge between nodes. The edge forms a network that connects two nodes together where the nodes of family, friends, and associates are now connected to both nodes. For example, Facebook is a digital network where two people can become friends on Facebook. Now, the friends of these two individuals are connected through a path that are formed by two individuals, unless the friends already have mutual friends. Ulises Ali Mejias states in Off the Network how “to ‘Friend’ in a social network that establishes a correspondence between two records of data (47). Every day, Facebook users are being connected together as one can find Facebook to consist of a giant component that encompasses every Facebook user being connected through edges. Even if one creates a new Facebook account, then they will soon friend someone else that will tie them into the giant component. Yet, the downfalls behind the giant component of Facebook include disruptive forces like a person impersonating a real-world friend that could disrupt the connection between you and your friends.

Furthermore, through Facebook, we witness various Facebook users discover their friends to have hidden mutual friends that may have not been discovered through technology. David Easley and Jon Kleinberg states this idea, small-world phenomenon, where the world looks “small” when you think of how short a path of friends it takes to get from you to almost anyone else (35). The downside from this phenomenon is it doesn’t mean you’re socially close to them.

David Easley and Jon Kleinberg, “Graphs” from Networks, Crowds, and Markets: Reasoning about a Highly Connected World (2010)

Ulises Ali Mejias, “Computers as Socializing Tools” from Off the Network: Disrupting the Digital World (2013)