  Recommendation algorithms for social media feeds often function as black
boxes from the perspective of users. We aim to detect whether social media feed
recommendations are personalized to users, and to characterize the factors
contributing to personalization in these feeds. We introduce a general
framework to examine a set of social media feed recommendations for a user as a
timeline. We label items in the timeline as the result of exploration vs.
exploitation of the user's interests on the part of the recommendation
algorithm and introduce a set of metrics to capture the extent of
personalization across user timelines. We apply our framework to a real TikTok
dataset and validate our results using a baseline generated from automated
TikTok bots, as well as a randomized baseline. We also investigate the extent
to which factors such as video viewing duration, liking, and following drive
the personalization of content on TikTok. Our results demonstrate that our
framework produces intuitive and explainable results, and can be used to audit
and understand personalization in social media feeds.
