Probing the Ethical Boundaries of Personalization: a Case Study
of Twitter’s Recommendation Algorithm
K.
Feng
,
M.
Ibrahim
,
and
J.
Yoon
Mar 2024
Most online content platforms today are designed to maximize the
time users spend engaging with their content. This engagement
allows platforms to both serve advertisements and collect data from
usage patterns to incorporate into their recommendation and per-
sonalization algorithms. However, personalization algorithms are
often opaque; surfacing relevant and interesting content to users
at best, and constructing echo chambers in which users are not
exposed to a diversity of opinions or beliefs at worst. This issue is
exacerbated by the fact that many content platforms enable users
to fine-tune personalization algorithms without much in the way
of ethical guardrails, such as “liking” a post or selecting “see less/-
more”. In this paper, we ask the question of whether there are
ethical limits to personalization in content platforms. Twitter is a
content platform with a wealth of publicly-available information
surrounding its personalization algorithms, which we use as a case
study for our investigation. We conduct a literature review of prior
research in content personalization on social media and analyze
published algorithmic personalization source code by Twitter along
with related technical blog posts. We then identify ethically nu-
anced components of Twitter’s content personalization pipeline
and analyze them from the perspective of 6 ethical theories. We con-
clude with a discussion of how developers can more deeply engage
with ethical considerations when building personalized systems.
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