My group and I are working on a project where we analyze specific twitter posts and search for specific keywords within these tweets. First, we are going to develop a program that can read through Twitter.com and collect every recent tweet. This program would have a filter on it allowing us to narrow down the tweets we collect. For each tweet that we obtain from the program, it would contain one of the filtered words. Now that the tweets are collected, we can do a deep analysis of the similarities & differences between them. Also, we can analyze the rhetoric and potential arguments behind each of them as we run our collected data through mapping software and Voyant Tools.
I am planning on working on optimizing the program that we develop. I would work with one of my other members to potentially develop the machine learning algorithm so that it can learn from the twitter posts that it reads and I will spend a lot of time on the analysis portion of the project in terms of the writing portion. My skill set is primarily focused in programming and development, but since we are a team of solid backgrounds, it seems like this project is going to move smoothly. We have already started to allocate roles and everything is working out at the moment since each of our members knows the goals behind our program and we each want to contribute to digital humanities in the same way.
In terms of technology we are using Voyant Tools, Twitter API, Python programming, data mapping software online, machine learning algorithms, and potentially HTML/CSS as we go on to publish our work on a website. Our project aims to shed a light on certain aspects of Twitter. Through the filters we use when collecting Twitter data and analysis, the patterns and correlations we find between tweets will allow us to point at injustices at both an online presence and offline presence. This would not only be based off of the scripted rhetoric on each of the tweets but would also come from media sources and other websites due to the fact that many of the posts we filter through have multiple interpretations. Filtering through this kind of data shows the way different online demographics are maintained and the messages behind their arguments point at social injustice at some level.