Research and Development

Crossmod is a bot created to moderate comments on Reddit posts by using machine learning classifiers that learn from past moderation decisions made in a variety of subreddits. I began working on this project after joining Dr. Eric Gilbert’s lab in early 2019. My first project on the team was to help project lead Eshwar Chandrasekhar and Chatrali Ghandi to conduct interviews on potential users of Crossmod (moderators on Reddit) and use the data we got from those interviews to adjust our direction for the project and the paper we were writing for CXCW on the project. This was my first experience with doing user research, and it taught me a lot about how to best conduct user research and use it to improve an end product. One thing I saw often while conducting these interviews was that many of Reddit’s moderators, despite using Reddit and it’s moderation tools often, couldn’t totally wrap their heads around how Crossmod worked, and how it differed from Reddit’s current moderation tools.

Early Design Work

Crossmod was the first real research project I had the chance to work on for a paper. At the time, I wasn’t really sure what I was getting myself into, but the topic of online content moderation was fascinating to me and, I developed a real belief in the project as I learned more about the methodology. After my first few months working on the project, I was assigned to create graphs, mock-ups and diagrams for the paper to illustrate how the Machine Learning program works and how users could potentially use it’s nuances to their advantage.

Eshwar was an amazing leader for our small group, brimming with ideas on different ways to visualize Crossmod’s complex systems. While this made coming up with concepts for visualizations fast paced and interesting, the concepts we were discussing in the abstract were often difficult for me to understand and visualize. Even drawings were often too complex for a layman to read. Given what I had learned from the interviews I’d done, I felt as though the average reader would be deeply confused, were I to simply copy verbatim what Eshwar had written. I worked hard to understand and asked a lot of questions, however, and in the end I was able to make some products that I felt were simple and understandable to the average reader.

Explanation poster

After the paper was published, your team’s next goal was to implement Crossmod on a real subreddit. Our team chose /r/Futurology, a subreddit dedicated to the development of new technology, as it seemed like the perfect place to debut a futuristic AI-backed technology. Many of the users on Futurology are skeptical of new technologies, however, and we wanted to create an infographic to explain to /r/Futurology’s users how Crossmod would work.

The visual presentation of this poster was important, as /r/Futurology’s users would likely not want to read more than a page on the new moderation platform. We needed to grab the readers attention and provide simplified statistics on how Crossmod would affect their Reddit usage. In order to do this, I used the data that we had already gathered to analyze how effective human moderators and Automod were compared to Crossmod. Using Python, my partner and I created many graphs to try to visually represent flagging thresholds and how Crossmod’s toxicity scores work. One of the most difficult parts of this project was deciding which of our visualizations to remove and which to keep, as we had so much data and so little space.

Balancing aesthetic beauty with informational value was also a big challenge in this project. Eshwar wanted the poster to be accessible, even to those outside of the academic sphere, so that he could hand it out at conferences or show it to other subreddits besides /r/Futurology. To make cuts, I thought a lot about where the reader’s eye would be drawn and how to use our colors to make the graphics pop.

While I have stepped away from the Crossmod project for now, as the team is currently working on the backend,