Where Does Rhetorical Distortion Live? A Multi-Platform, Multi-Language Measurement of Influencer Content

Xucheng Yu, Jiangping Chen, Haohan Wang

Much of the manipulation in online influencer content is not false but disproportionate: an accurate fact is framed as historic, urgent, or catastrophic. Fact-checking and clickbait detection are not designed to capture such framing, and little is known about where it originates: with the creator, the topic, the platform, or the audience that replies. We operationalize rhetorical distortion through five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion) and measure it with a three-tier pipeline in which bilingual lexical rules propose candidates, negative filters cancel known false-positive contexts, and an LLM verifies or relabels the candidates. Applying the pipeline to 13,060 posts from 330 accounts on six platforms (RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter), we find that platform is far more strongly associated with account-level distortion than content category (Kruskal-Wallis $\varepsilon^2=0.33$; two-way ANOVA $η_p^2=0.49$ for platform vs. $0.09$ for category). X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). Classifying tweets and their replies separately shows…