The goal of my team’s project is to better understand and visually express attitudes towards racial relations via Twitter. To help accomplish this, my group will utilize a program that collects tweets that use certain key words and Voyant, a program that assists in text analysis. The end result of the project will likely be some form of a website that hosts our program, our findings (in the form of graphs, charts, and clusters (?)), and our analyses. Some of the variables we will test are: support for Black Lives Matter, geography, social location, and trends.
To date, my role in the project has largely been helping to think through analytical goals, testable variables, and searchable terms. My greatest strength is my analytical capacity related to issues of social justice. As a result, I think I will have two main contributions from here on out. First, I will likely sift through several thousand tweets at some point to determine their attitudes toward Black Lives Matter. We hope to train a program to conduct sentiment analysis, but that will likely be difficult given Black Twitter’s sarcastic attitudes toward issues of racial concern. Second, I will probably play a large role in writing our supporting documentation. I feel confident in my ability to articulate connections between our work (product AND process) and broader issues of social justice. Three of my teammates have relative strengths in programming and data visualization, while another one seems to favor analytical deliberation over issues of social justice. This project is a great opportunity for the two “humanists” (including myself) to collaborate with the three “computer scientists” in developing clearer end goals, refining processes to be more socially just, and analyzing our results in means consistent with our digital and technological outputs and constraints.
The various technical components of our project include: a program for mining tweets, Voyant, Twitter, a host website, supporting written documentation, and traditional modes of data visualization. All of the relevant technology we will draw upon, from our computer programs to our own analytical frames, are based in Western epistemic and ontological frames. For example, data visualization is typically used for neoliberal instruments of profit-maximization. Even though it is not clear to us yet what Twitter’s contributions to social injustices are, I am sure the technological components of Twitter reflect certain biases and knowledge-producing practices that operate under the same logic as those underlying certain social inequities. Even our program and our findings could be repurposed for socially unjust ends. For example, white supremacist groups could use our findings on support for Black Lives Matter to illustrate the “domestic terror threat” that the group poses. As a group, we need to understand the historical and spatially unjust modes of usage for these technologies, tools, and methods and rearticulate them for progressive means. For instance, by using traditional data visualization techniques to illustrate issues of racial injustice, we are able to use typically exploitative tools in socially just ways. That mere process of rearticulating neoliberal and racist tools is critical in the Digital Humanities writ large. Hopefully our project can play a role in that larger, ongoing project.