arXiv cs.CY· Xucheng Yu, Jiangping Chen, Haohan Wang·· 11 小时前AI 评分31
修辞失真藏在哪里？跨六平台、双语对网红内容的多平台测量
Where Does Rhetorical Distortion Live? A Multi-Platform, Multi-Language Measurement of Influencer Content
AI 导读
一项跨六平台、双语的研究用同一套三级流水线测量网红内容的修辞失真，将创作者帖子与回复分开分类，LLM 仅对 14% 的帖子进行核验。在 330 个账号的 13,060 条帖子上，平台与账号级失真的关联远强于内容类别（partial η²=0.49 vs. 0.09），X/Twitter 得分最高（DI 7.74，RSS 仅 0.78）。
正文 · 原文
Abstract:Much of the manipulation in influencer content is not false but disproportionate, framing accurate facts as historic, urgent, or catastrophic. Existing approaches either verify factual claims or detect clickbait and propaganda in single-platform, single-language text; however, they overlook where such framing originates, because they treat a post and the replies beneath it as one unit. To address this gap, we measure rhetorical distortion with one instrument applied identically across six platforms and two languages, classifying creator posts and their replies separately. Our three-tier pipeline detects five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion): bilingual lexical rules propose candidates, negative filters cancel known false positives, and an LLM verifies or relabels the candidates. This design keeps labels transparent and comparable across platforms, calls the LLM on only 14% of posts, and supports validation against human annotators and a second model. On 13,060 posts from 330 accounts on RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter, platform is far more strongly associated with account-level distortion than content category (partial $\eta^2=0.49$ vs. $0.09$), and X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). On X/Twitter, the top-5 replies under a post carry 3.5x the distortion of the tweet they answer (1.5x at equal length), while creator tweets alone are indistinguishable from YouTube and Bluesky; two human annotators and GPT-4o confirm this gap. Measured distortion on threaded platforms thus lives largely in the audience layer, which calls for measuring creator and reply text separately.
| Comments: | 12 pages |
| Subjects: | Social and Information Networks (cs.SI); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.26412 [cs.SI] |
| (or arXiv:2610.26412v2 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.26412 arXiv-issued DOI via DataCite |
Submission history
From: Xucheng Yu [view email]
[v1]
Mon, 19 Oct 2026 13:47:02 UTC (80 KB)
[v2]
Tue, 27 Oct 2026 16:31:08 UTC (1,127 KB)
来源：arXiv cs.CY · arxiv.org