Exactly why are these responses similar?
The most common visualization where all the nodes connect is, Johnny B. Goode. This is evidenced by how close to the center the song is to all the people who are in class. The central node that brought all of us together was this song captured in the network of everyone in class. In Code.org (2017), this central node would be equivalent to everyone having the same content in each of our pages, or in this case our assignment. The other content within our networks’ circle are Jaat Kahan Ho and the Brandenburg Concerto. While both are close to center, each one is pulled above and below the center, meaning that they are close to equal with 8 people in the class listing Brandenburg Concerto and an equal part listing Jaat Kahan Ho in their assignment. We can relay that the three have shared interested within class as each of the three having a very strong correlative effect with the members of the class as how they fall in line to what would be a central axis (Systems Innovation, 2015).
Is the visualization able to capture the reasons behind the choices?
The visualization might be able to capture the choices and reasons behind the choices. In a sense, having a center means that we all have a commonality with the choice of songs. Johnny B. Goode could be chosen for many reasons among the class and we could stipulate that there’s meaning that every member of the class has with that song choice. I could stipulate that it had the most energy, it could be the most recent song, it could have the right tempo or philosophy and it could be all those things as it showed up in everyone’s. The visualizations also show songs that are unique to other people’s choices as evidenced by the location of the nodes terminating from the users and not within the web. In a sense, I could get stories based on my own analysis of previous content, but nothing too overt, I could recognize that having a song in the center means commonality and anything outside of the web is one that’s unique to each user and could be interpreted in what makes each list unique, like a keystone search.
Why did the people not choose other pieces?
As alluded in the post above, the visualization of the graph shows that there are pieces that were chosen specifically by that person and nowhere else. This piece of data shows the differentiating point and holds important data about how the set was made. While we can’t surmise the exact reason that songs were on the outside, just like a cypher, these individual data sets could point to a reason why it’s chosen.
Can the reasons for these “null” choices ever be reflected/interpreted in the data?
In a sense, reasons for null choices can be reflected and interpreted in the data based on where it is spatially in the web (Code.org, 2017). The null choices can be determined can be based if you knew the person that the song was attached too and just like a cypher, these null choices become evident as a key that can interpret the themes for the song choices. However, that requires an in-depth analysis of what’s in the web of choices and knowing about the person that the null choices have a chance of being interpreted.
References
Code.org. (2017, June 13). The Internet: How search works [Video]. YouTube.