Week 11: Finalizing My Project
May 9, 2026
Hello everyone, and welcome back to my blog! This week, I mainly focused on preparing and giving my practice presentation for my echo chamber project. After spending the last few weeks building my model, comparing Reddit and X, and analyzing my results, this week was more about putting everything together into a presentation that people could easily follow and understand.
One thing I realized during my practice presentation was that it can be difficult to explain technical ideas in a simple way. Since my project includes network analysis, sentiment analysis and topic homogeneity, I had to think carefully about how to explain each part clearly without making it too complicated. I also worked on improving how I explained my graphs and results instead of just showing numbers on the screen.
A big piece of feedback I got was to better explain why network isolation is the most important part of my model. Because of this, I spent more time reviewing my results and making sure my explanations connected back to my data. I also improved the way I explained the differences between Reddit and X, especially how X is more based on user networks and algorithms, while Reddit is more focused on topic communities and discussion threads.
Besides working on my presentation, I also spent time looking into more research papers related to online polarization and echo chambers. One paper I looked at was The Spread of True and False News Online by Soroush Vosoughi, Deb Roy, and Sinan Aral in Science. The paper discussed how emotional and surprising information spreads faster online, which connects to some of my own findings about sentiment similarity and reinforcement on X.
I also researched more about how recommendation algorithms can increase polarization by repeatedly showing users similar content and viewpoints. A lot of the research I found supports the idea that platform structure itself plays a major role in creating echo chambers, which matches the conclusions I found from comparing Reddit and X.
Another interesting thing I started looking into was how echo chambers change over time. Some researchers study how polarization increases during elections or major world events. This made me think about possibly expanding my project in the future to see how echo chamber values change during important events instead of only looking at one snapshot in time.
Overall, this week helped me focus more on communicating my research clearly and connecting it to larger ideas in current research. The practice presentation helped me figure out which parts of my project are strongest and which explanations I still need to improve.
Thank you for reading, and I will see you all next week!
Harish
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Hi Harish, do you think that the methodology you used in this project could be used to analyze the polarization/echo-chambery levels of different communities before, and after world events? For example, if there was a large sporting event, would you be able to quantify the effect that event had on online communities by measuring the difference in how much of an echo chamber that community was before and after the event?
Hi Anav. Thank you for your comment! Yes, this methodology can definitely be used to model echo chambers between time frames. The main part that needs to be changed is the comments that are being inputted. If you were to access comments from specific time frames, you can use this framework to compare not only how echo chambers might change over time, but also varying topics between times.