Auditing Differential Visibility of Political Content on TikTok

Abstract

Allegations that TikTok shadow bans political content shape what creators post, what advertisers fund, and how regulators act, yet they are hard to adjudicate because platforms do not disclose how content is ranked. We test the claim with a dense hourly panel of 556,946 follower-normalized views across 2,753 videos from 67 accounts curated into pro and anti sides of three contested topics (U.S. immigration enforcement, Trump coverage, and Israel/Palestine). On-topic videos are identified by a multi-step classifier, and stance is taken from each account's curated side. The conventional analysis appears to answer yes. Pooling the hourly snapshots, the topic-conditional reach gap reaches p<10^-140. Analyzed at the account level, the independent unit at which we sample and assign stance, the gap disappears. Every account-level reach contrast is null after correction (BH-FDR q near 0.9). We find no evidence of moderate-to-large reach suppression on any topic. The null is informative. Account-level confidence intervals and a power analysis rule out such effects. As a design check, the same framework detects a clear asymmetry on a different outcome. Oppositional content (anti-Trump, pro-Palestine) earns more engagement per view rather than less reach (Cliff's delta = -0.51 and -0.64; q<0.03). Higher engagement does not by itself rule out suppression, but shows the design can detect effects of this magnitude. The apparent reach gap is an artifact of two factors. The first is pseudoreplication, which counts tens of thousands of autocorrelated video-hours as independent observations; the second is confounding, since the side that looks suppressed is larger and, on Israel/Palestine, posts mostly in Arabic. In this corpus, what is taken for a shadow ban is better explained by a more engaged audience than by a suppressed one. We close with what a credible visibility audit requires.