Author Archives: Eric

Well What is Activism?? – Eric S.

Posted on by
Reply

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.

OneTrust. (2020). Privacy 101: Data Subject Rights [Video]. YouTube. https://www.youtube.com/watch?v=C3hio0WkRHg

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.

Eric S : Project Proposal Blog Post

Posted on by

For my final project, I am absolutely clueless about what I want to create a digital humanities project on. I know I want to create data visualizations and am open to story maps; however, I don’t know what data to use. To start generating ideas, I went to www.kaggle.com to search for a dataset to help me form some rough ideas. So far, the datasets that I am interested in transforming into a project are: 

  • Online news Popularity
  • Instagram Reels: Virality & Engagement Trends 2026
  • Messy E-Commerce Sales Data
Burg, M. (n.d.). Abstract pink purple 3D Render. unsplash.com. https://unsplash.com/photos/abstract-pink-purple-3d-render-kE8-rUKjtQU

I actually have come up with a very crude digital humanities question; however, I am not very confident in its feasibility or whether it would be complex enough for the class. The project would be exploring the history of The College of Wooster student organizations’ political activism as reported in the Wooster Voice from the years 2000 – 2020. For this possible project, I would literally search all the Wooster Voices published between the start of the 2000 academic year and the end of the 2020 academic year. This question arose after this past weekend being homecoming, and there were a lot of alumni who spoke about their respective student organizations being very politically active off-campus. I’d mainly be looking at how invested was the student body about these policy changes as off-campus protesting is heavily restricted at the college today.

LLM Post: Eric S

Posted on by

The most interesting thing that I’ve learned about Language Learning Models(LLMs) this past week was that they don’t actually try to predict the right answer for the prompt, but that they purely go off of statistics with a little bit of context. I found the term ‘Stochastic Parrot‘ from Emily Bender’s lecture to be a good nickname for the process that LLMs use to answer any given prompt; similar to real parrots, they just repeat and string together words and/or sentences without knowing the meaning behind them. 

3Blue1Brown. (n.d.). Transformers, the tech behind LLMs | Deep Learning Chapter 5 [Video]. YouTube. https://www.youtube.com/watch?v=wjZofJX0v4M

I found the video above from 3Blue1Brown to be a very understandable overview(for someone who hates computer science) of a bit of the background and process of transformers to LLM’s to the generative AI that we see now. And it really helps to illustrate not just how they work but also alludes to how they can go wrong and create negative feedback loops and hallucinations mentioned in the article by Jon Cheung, ‘How AI Image Generators Make Bias Worse.’ With how AI datasets are bound to have some form of bias in them, and then as generative creates more content representative of that bias, it then feeds back into itself, amplifying the biases even more.

Citations:

  • 3Blue1Brown. (n.d.). Transformers, the tech behind LLMs | Deep Learning Chapter 5 [Video]. YouTube. https://www.youtube.com/watch?v=wjZofJX0v4M
  • Lis, & Lis. (n.d.). How AI image generators make bias Worse. London Interdisciplinary School. https://www.lis.ac.uk/stories/how-ai-image-generators-make-bias-worse
  • Harvey Mudd College. (2024, November 20). Emily Bender: “Don’t Try to Get Answers from a Stochastic Parrot” | 2024 Nelson Speaker Series [Video]. YouTube. https://www.youtube.com/watch?v=5m0ZolIb2hA
  • Declaration of AI Use: I acknowledge the use of Generative AI tools in writing this post: I used Grammarly to refine the language after drafting on my own, I declare that I reviewed and edited the contents as needed and take full responsibility for the content of my submission. All of the information I presented is complete and accurate.

Black Super Bowl = Genre Streams???

Posted on by

Many artists and people see the Super Bowl as the pinnacle of music popularity. So much so that the Super Bowl doesn’t even pay the artists for their performances; it is almost purely for “exposure.” Now that all forms of media are monetized, artists are very likely to gain some revenue from their Super Bowl performance anyway, but the main goal is increased media presence. In this day and age, there are two main standards to meet in order to be regarded as a popular or successful artist in the US: Grammy awards/nominations, and streams. Streaming apps such as YouTube, Spotify, Apple Music, etc, have almost completely rendered physical media useless. It has become the way that major music corporations across the world now use to gauge success.

With the Super Bowl being one of, if not the largest, stages for an artist, I wanted to see how much of an impact a Super Bowl performance has on the number of streams of that artist’s particular genre within that year, and if there are any notable changes from the year prior or after. And then I further made the decision to only go off of the black Super Bowl artists from 2010 – 2023, as there has been a higher frequency of black artists being chosen to perform for the Super Bowl. Then, after identifying each black artist or group that was predominately black, I then classified them into only one genre. This was met with some difficulty because the data and Spotify itself limit one artist to one genre, even though their music career may span over many different genres. So I just classified them into a genre that fit their music the most at that time, so example, even though now most will say that Bruno Mars makes RnB and Soul music, the majority of his music circa 2014 was pop. Although I still chose to have Bruno Mars and Beyonce cross listed with pop and RnB. Another hiccup in this was the year that many different artists that were under Death Row Records headlined in 2022. The artists were: Dr. Dre, Snoop Dogg, Mary J Blige, Eminem, and Kendrick Lamar. While Mary J Blige is infamous for her RnB records, as the majority of artists and the theme around this year’s Super Bowl performance were California Rap and Hip-Hop, it was labeled under the Hip-Hop/Rap genre.

chart visualization

The graph above was created using the aggregated dataset mentioned previously. I made sure to emphasize which year there was a black artist during the Super Bowl and the name of the artist. As you can see above, using the pop genre as an example because it has the most Super Bowl performances, after 2019, all streams went down collectively, and the Super Bowl performances for the following years were no exception. This can be seen for all other genres as well. In conclusion, based on the data, there is no significant change in the number of streams per genre based on the black Super Bowl artists’ performances of that year after 2019.

While my question did not lend any new and profound knowledge to the world, this project has taught me a lot about data literacy and visualization. The main shift in my thinking about data is determining what data is necessary and unnecessary. The entire dataset includes the streams for genres not shown above, and when I was looking at all the categories, I got lost in what I was supposed to find, which made it harder for me to generate a question from it. I often have trouble creating interesting and achievable research questions. Because of my refusal to leave out information; I tend to find myself trying to incorporate everything and every possibility into my research, which is often times impossible. So instead, with this project I made the conscious and honest decision to only ask questions about what I was personally the most interested in. This also allowed me to analyze my own visualizations’ legibility with graphic design practices such as color, contrast, and typography. Making sure that the colors used in the graph were distinguishable from one another and that the axis and labels were legible. If I were to do this project again, I would first find another question to ask that went deeper into data manipulation and context. Reorganizing or using the data more creatively and knowing when, where, and how the data was sourced, as the data gathered does limit the breadth of questions that can be generated.

Citations:

–

Eric S. Data Blog Post

Posted on by

For this blog and data questions I used the February 7th, 1891 edition of the Wooster Voice, edited by R. H. Herron and A. Fullerton. This edition was very notable as it was written shortly after the voices inception in 1883 and it is very clear that during this period, the voice was still finding its voice. Some questions that were generated from this edition were: How often were/are non standard English conventions and other languages featured in the Voice; As well as how often other colleges and universities mentioned in the voice and for what reason. These questions stemmed firstly because in the February 7th, edition, there is an entire column written in what seems to be pig latin, and there is an entire section dedicated to the happenings of other universities such as Yale, Columbia, and Dartmouth.

Editors, Wooster Voice, “The Wooster Voice (Wooster, Ohio), 1891-02-07” (1891).The Voice: 1890-1900. 18.
https://openworks.wooster.edu/voice1890-1900/18

If I were to create a data sheet, tracking how often other colleges were mentioned in the voice I would make columns for: name of college, date, create a keyword library for how they were mentioned or what they were mentioned for in simple categories(sports, student organization, staffing changes, etc.), and then a summary column quoting what they were mentioned for.

In this proposed dataset I do not think that privacy would be much of a concern as much of the information spoken about in this section does not mention specific people by name except for the case of a university hiring a new professor or president. Instead I would be very careful and diligent in my citing of each edition of the Wooster voice used in the data set.

Introduction

Posted on by

Hey y’all, Im Eric (any pronouns) and Im from Atlanta, GA and I am looking to major in GMDS and double minor in Math and Africana Studies. I have too many hobbies to count but I’d say the one that I’m into right now are music, tennis, and dnd.

Image taken from Pinterest; Author unknown

My only muddy point is just what is DH, but I’m excited to figure that out through the class.