The first thing I noticed is that my name seemed to have many connections to it. This could mean that my music choices were similar to others. I also noticed 2 songs on the outskirts of the visualization that had only 1 and 2 connections meaning they were not chosen by many. Looking into the metrics section I saw that many people were similar to me in thinking that Johnny B Goode was a great song as it received 16 and had the highest degree of connectivity along with Melancholy Blues. The fact that both of these songs had such strong connectivity indicates that the majority of people in this course may be North American. Or it could indicate how prolific American culture and music is across the globe. I know I found it hard to remove one of these songs to make room for another because they were so familiar to me.

When looking at the groupings of responses it was not possible to capture the reasons behind these choices. In my group (0) there were 4 of us. There were many songs that only one person had selected and there were some many of us selected. Because of this I do not think it was based on musical taste or even a similar song selection process. I do not think it is possible for the visualization to capture the ‘why’ behind each person’s choices.

These visualizations do not capture the reasons for why people did not choose certain music. These null choices could be influenced by many things such as culture, exposure, and personal preference. They also assume similarity based on our music selections when really we are all so different and probably made these selections based off of different motivations or rationale. Additionally, the null choices may represent cultures that are underrepresented. The fact that 2 American songs had the highest connectivity show how this could be perpetuated in other ways through the web. This is a good reminder that drawing conclusions about people based off of data, especially when so limited, it is problematic. This quantitative rather than qualitative data can allow for stereotyping and generalization, bias, and misinterpretation.