This is the second part of a two-part series about a class project on online filter bubbles. In this post, where we focus on the results. You can read more about our pedagogical approach and how we carried out the project here.
By Janet Xu and Matthew J. Salganik
This past spring, we taught an undergraduate class on social networks at Princeton University which involved a multi-week, student-led collective class project about algorithmic filter bubbles on Facebook. We wanted to expose students to the process of doing real research, and filter bubbles seemed like an attractive topic because they are interesting, important, and tricky to study. The project—which we called Breaking Your Bubble—had three steps: measuring your bubble, breaking your bubble, and studying the effects. In short, all 130 undergraduates in the class measured their Facebook News Feed for four weeks—recording the slant (liberal, neutral, or conservative) of the political posts that they saw. Then, starting in the second week of the project, students implemented procedures they had developed in order to change their News Feeds, with the goal of achieving a “balanced diet” that matched the baseline distribution of what is being shared on Facebook. Students also came up with public opinion questions for a big class survey, which they took at both the beginning and the end of the project. You can read more about what exactly we did, how it worked, and what we’d do differently next time here.
Though our primary goal was to teach students about doing research, we also learned some surprising things about the Facebook News Feed from the aggregated student results.