Why tackle Filter Bubbles, you ask?
Post-Election Pivot & Honing in on Social Media Platforms
On the evening of November 8th, the world as I knew it was turned upside down. I was forced to face the fact that my reality was merely one version among many, that it wasn’t the one that a majority of America was living. I was disillusioned and completely blindsided, and after slogging through the five stages of grief, I landed not on acceptance but on guilt. I felt guilty for having been perfectly comfortable inside my little bubble — the bubble that was shared, I think, by others that could afford to turn away from the drudging life of those that never recovered from the ’08 recession, from those who were promised a better life for themselves and for their children but was never delivered one, and were shrugged off by people who pursued the question of “who will get to use what kind of bathroom?” instead of lending a sympathetic ear to the Other side of the U.S. population.
I decided to come full circle and focus my attention back on Filter Bubbles and Social Media platforms that exacerbated them, and this is what informs my design thesis proposal.
It’d be impossible to avoid Machine Learning (ML) and bubbles, but if I could visualize the way machine learning works for SM platforms and search, and how the bubble gets created and how they contribute to the creation of their own bubbles, maybe users will navigate more consciously. So, my hypothesis is that:
The visual delivery of social media platforms (and/or search engines) is promoting a singular perception of various different types of information, and is therefore aggravating the effect of the “bubble.”
