{"id":149,"date":"2025-03-09T14:12:09","date_gmt":"2025-03-09T21:12:09","guid":{"rendered":"https:\/\/blogs.ubc.ca\/keizern\/?p=149"},"modified":"2025-03-09T14:12:10","modified_gmt":"2025-03-09T21:12:10","slug":"network-assignment-using-golden-record-curation-quiz-data-task-9","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/keizern\/network-assignment-using-golden-record-curation-quiz-data-task-9\/","title":{"rendered":"Network Assignment Using Golden Record Curation Quiz Data Task 9"},"content":{"rendered":"\n

To better understand the visualization, I first highlighted the source nodes\u2014our names\u2014which allowed me to distinguish between curators and song titles. I then rearranged the nodes by spreading out all the curators in a U-shape. This made it easier to see which songs were the most popular based on node size and which songs had fewer selections. <\/p>\n\n\n\n

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By moving a specific track node, I could observe how many and which people had chosen it. For example, Track 13 had five people select it. <\/p>\n\n\n\n

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When I slightly offset Track 24 next to Track 13, I noticed that four of the five people who selected Track 13 had also chosen Track 24. However, the visualization does not provide any indication as to why these selections were made.<\/p>\n\n\n\n

I found it challenging to interpret connections with so many nodes and edges. It would be helpful if the visualization incorporated color-coding. For example, if selecting a source node changed its color, along with all its corresponding edges, and a different source node triggered a different color, patterns might be easier to identify.<\/p>\n\n\n\n

In community one, I used a similar method of node arrangement and observed that all five curators in the group had chosen Track 7. Additionally, several other tracks were selected by three or four of the five curators. However, again there is no clear way to determine why they made these choices. Is there a common factor among these curators that influences their selections? Another important question that the given visualization cannot answer is whether all curators chose a particular track for the same reason. In fact, there may be no real similarity between these curators if they all selected the same track but for entirely different reasons.<\/p>\n\n\n\n

This dataset contained 27 tracks, and all of them received votes, with the least popular having two votes and the most popular having 15. However, if some tracks had received no votes, they would not appear in the visualization at all. This absence could prevent viewers from considering why those tracks were not chosen.<\/p>\n\n\n\n

Ultimately, the visualization provides only basic connections without deeper explanations. With additional data\u2014such as age, gender, location, or musical preferences\u2014it might be possible to identify relationships between song choices and external factors. This highlights why so much data is collected about us. More data enables a deeper understanding of connections and can be used to influence behaviors, such as purchasing habits or even political opinions. If advertisers can determine what persuades one group to click on a link and buy a product, they can apply similar tactics to other groups with shared characteristics.<\/p>\n\n\n\n

It is unsettling to consider how our thoughts and decisions are influenced\u2014or attempted to be influenced\u2014without our awareness, shaped in part by our network connections.

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

To better understand the visualization, I first highlighted the source nodes\u2014our names\u2014which allowed me to distinguish between curators and song titles. I then rearranged the nodes by spreading out all the curators in a U-shape. This made it easier to Continue reading Network Assignment Using Golden Record Curation Quiz Data Task 9<\/span>→<\/span><\/a><\/p>\n","protected":false},"author":91876,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-149","post","type-post","status-publish","format-standard","hentry","category-weekly-tasks"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/posts\/149","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/users\/91876"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/comments?post=149"}],"version-history":[{"count":1,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/posts\/149\/revisions"}],"predecessor-version":[{"id":150,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/posts\/149\/revisions\/150"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/media?parent=149"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/categories?post=149"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/keizern\/wp-json\/wp\/v2\/tags?post=149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}