Blog post 4: Information visualization & distant reading
In Chapter 6, “Information Visualization,” I gathered a lot of information about considerations that factor into graphs and networks. Visualization of metric data is represented by graphs, and it may be either discrete or continuous. Discrete data is quantitative data. For example, height of an individual. Continuous data can be connected by dots on a graph so that it makes sense to a viewer. For example, a graph of change in height over time for an individual. Axes, elements, scales, order/sequence, and graphic variables join to create a product that is solid and visually understandable. Visualizations are also useful for revealing patterns, anomalies, and other features of data to the eye. Though visualizations are interpretations rather than factual displays, appropriate features need to be chosen during the design process to avoid the creation of a misleading graphic.
In “What is Distant Reading,” Kathryn Schulz gives insight into the arguments being posed for distant reading. In “Problems of Scale,” Jay Jin explains how distant reading is an ideology that has emerged in what seems to be a counterculture against “close reading.” Close reading was previously adopted in literary analysis, and it emphasizes examining a smaller number of texts with an observant, analytical eye. Distant reading completely opposes this method; the Stanford Literary Lab makes it possible for a large network of texts to be analyzed against one another. Thereby, scholars can understand patterns, networks, and trends across a broad range of literature without ever reading a single work. I think that the idea of understanding a plethora of works of literature without the contribution of any time or effort is a tempting idea as a reader, and there is great potential in this field. However, I disagree with Moretti’s statement that distant reading should “supplant” close reading. It makes sense for distant reading to supplement close reading to situate the text within the broader picture, but both are necessary for understanding. For instance, I can explore “Six Degrees of Francis Bacon” forever, but my knowledge is useless if I have not read any of his or his connections’ work.
Using Voyant tools was interesting because it was effective at creating visual trends, but these visualizations were not useful to my understanding. Seeing how frequently certain words are used in a book, for example, does not help me grasp the plot. I wonder in which context this tool is the most useful.
I really like your interpretation of continuous data and how it relates to the fluidity of being able to read data because like mentioned, the extra variables like axes, elements, scales are a key piece to further help make reading data as easy as possible. With the right elements in the right spots, reading data is much easier because you don't have to think as much about the actual statistics in the data and can interprete pieces of data like graphs much quicker. I interpret it like a language, we grow up hearing English speaking and because of the frequency of this we easily learn English. The same applies to discrete data, and I liked how you incorporated the Stanford Literary Lab and its data because it provides an interesting take that we can interpret sophisticated data with low effort, and I think it really depends on the text, because I think we are generally more inclined to understand data about things we like easier.
ReplyDeleteI really liked your post! I agree with you on how Chapter 6 explores the nuances of information visualization, highlighting the importance of discrete versus continuous data representation factors. I think that visualization reveals certain patterns but also demands careful design to avoid misinterpretation and miscommunication. I also agree that distant reading offers a broad lens for literary analysis.
ReplyDeleteI learned a lot from your analysis! I especially enjoyed how almost the entire post is factual and informative, besides the bottom paragraph, which gives a nice human connection at the end. I thought the whole post also had great flow, with your main points regarding how people use continuous data in the center paragraph. In this center paragraph, you establish a thesis of sorts, being that scholars can understand works without ever having read them through the power of data visualization. Compared to other posts on Chapter 6, I feel as if this one most clearly establishes the main points of the chapter without under or over explanation.
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