My final digital humanities project will focus on the History of Student Organization Political Activism as reported in the Wooster Voice from 2000 to 2020. For this final project, I will manually read through every publication of the Wooster Voice from the start of 2000 through the end of 2020, identifying political acts from different student organizations and classifying them into two categories: on/off-campus and location (local/regional/national/global). I will create visualizations of this data using Flourish to explore changes in college policy on school-sanctioned student political activism and the political events that drew Wooster students’ attention at the time. This project does have three ethical concerns that need to be addressed: data collection and privacy, community representation, and accessibility/universal design.

I first want to address community representation with this quote from Yau: “You’ve seen how that data can be valuable to an individual. What if you look at the data from many individuals in aggregate?” One main concern with this project is that it does not represent the entire campus community at that time. Because I am sourcing this data from archives of a student newspaper, my research is limited to what the reporters, writers, and editors of the Wooster Voice cared about and were involved in. Another consideration is that the political state of the nation, or even the surrounding Wooster area, also affects what gets reported. So my project is based on the personal lives and interests of the many authors of the Wooster Voice from 2000 to 2020, as well as what I consider to be activism.

As I am the only person working on this project, another thing that I, as well as any reader, must consider is my personal bias. “The easiest way to show off with spectacular data is to fabricate it.”-Kayser-Bril. While I personally have no intention to purposefully misconstrue data in any way, I may inadvertently do so, or the writers of the student newspaper may have. As we’ve seen in discussions of data collection paired with our deep dive into Language Learning Models(LLMs), there is no way to escape bias of any sort. The writers’ personal bias on what to report on and my personal bias on ‘what is activism?’ is what makes this project just a very brief and topical analysis. In order to help circumvent some of my own personal bias, whenever I encounter a story about a student organization and am hesitant on whether or not it is a form of political protest, I will ask either a peer or a person of authority for their opinion. This is not a perfect plan, nor is it free from error or bias; however, it’s the best I got.

My final consideration is on the presentation of this project in general int terms of who can and/or can view, consume, and understand it. Firstly, when it comes to the design of the data visualizations themselves, I will ensure that the design is inclusive, especially with color and typography. Readability as well as digestibility are the two most important things when it comes to communicating with data. Providing an interpretation for the data is necessary; however, viewers should also be able to understand and draw conclusions from the data themselves. As this project is limited to this class, my personal goal is that it’s accessible to that and similar audiences; this is to say that the project will not be inclusive on a national level.

Citations:

• Ervin, W. (2013). Data Points: Visualization That Means Something. Nathan Yau. Information Design Journal, 20(3), 294–295. https://doi.org/10.1075/idj.20.3.09erv
• DataJournalism.com. (2019, February 6). Become Data Literate in 3 Simple Steps. DataJournalism.Com. https://datajournalism.com/read/handbook/one/understanding-data/become-data-literate-in-3-simple-steps
• OneTrust. (2020). Privacy 101: Data Subject Rights [Video]. YouTube. https://www.youtube.com/watch?v=C3hio0WkRHg
• Declaration of AI Use: No AI was used in the ideation or construction of this project, gram marly was used to correct grammar.