| metadata_version: 1 | |
| name: Short-term exposure to filter-bubble algorithmic recommendations have limited | |
| effects on polarization | |
| description: An enormous literature argues that recommendation algorithms drive political | |
| polarization by creating "filter bubbles" and "rabbit holes." Using four experiments | |
| with nearly 9,000 participants, we show that manipulating algorithmic recommendations | |
| to create these conditions has limited effects on opinions. Our experiments employ | |
| a custom-built video platform with a naturalistic, YouTube-like interface presenting | |
| real YouTube videos and recommendations. We experimentally manipulate YouTube's | |
| actual recommendation algorithm to simulate "filter bubbles" and "rabbit holes" | |
| by presenting ideologically balanced and slanted choices. Our design allows us to | |
| intervene in a feedback loop that has confounded the study of algorithmic polarization—the | |
| complex interplay between *supply* of recommendations and user *demand* for content—to | |
| examine downstream effects on policy attitudes. We use over 130,000 experimentally | |
| manipulated recommendations and 31,000 platform interactions to estimate how recommendation | |
| algorithms alter users' media consumption decisions and, indirectly, their political | |
| attitudes. Our results cast doubt on widely circulating theories of algorithmic | |
| polarization by showing that even heavy-handed (although short-term) perturbations | |
| of real-world recommendations have limited causal effects on policy attitudes. Given | |
| our inability to detect consistent evidence for algorithmic effects, we argue the | |
| burden of proof for claims about algorithm-induced polarization has shifted. Our | |
| methodology, which captures and modifies the output of real-world recommendation | |
| algorithms, offers a path forward for future investigations of black-box artificial | |
| intelligence systems. Our findings reveal practical limits to effect sizes that | |
| are feasibly detectable in academic experiments. | |
| authors: | |
| - name: Naijia Liu, Xinlan Emily Hu, Yasemin Savas, Matthew Baum, Adam Berinsky, Allison | |
| Chaney, Christopher Lucas, Rei Mariman, Justin de Benedictis-Kessner, Andrew Guess, | |
| Dean Knox, and Brandon Stewart | |
| affiliations: | |
| - name: Multiple | |
| corresponding_contributor: | |
| name: Dean Knox | |
| email: dcknox@upenn.edu | |