File size: 2,317 Bytes
649d4d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | 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
|