AutoLike: Auditing Social Media Recommendations through User Interactions
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Modern social media platforms, such as TikTok, Facebook, and YouTube, rely on
recommendation systems to personalize content for users based on user
interactions with endless streams of content, such as "For You" pages. However,
these complex algorithms can inadvertently deliver problematic content related
to self-harm, mental health, and eating disorders. We introduce AutoLike, a
framework to audit recommendation systems in social media platforms for topics
of interest and their sentiments. To automate the process, we formulate the
problem as a reinforcement learning problem. AutoLike drives the recommendation
system to serve a particular type of content through interactions (e.g.,
liking). We apply the AutoLike framework to the TikTok platform as a case
study. We evaluate how well AutoLike identifies TikTok content automatically
across nine topics of interest; and conduct eight experiments to demonstrate
how well it drives TikTok's recommendation system towards particular topics and
sentiments. AutoLike has the potential to assist regulators in auditing
recommendation systems for problematic content. (Warning: This paper contains
qualitative examples that may be viewed as offensive or harmful.)
