arXiv cs.CY· Hunjun Shin, Hongpeng Fu, Divya Chaudhary, Mohit Singhal·· 12 小时前AI 评分34
TikTok 食物恐惧内容研究：恐惧不靠病毒式传播，而是持续更久
Fear Doesn't Go Viral, It Lingers: Spread, Affect, and Discourse of Food-Fear Content on TikTok
AI 导读
一项基于 97,234 条 TikTok 食品营养视频的研究发现，恐惧类视频触达人数反而少于非恐惧视频，但每播放分享量高 1.7–2.1 倍，评论半衰期长 1.62 倍。研究用 Gemini 3 Flash 构建零样本多模态分类器标注数据（Cohen's κ=0.81），并分析超 110 万条评论。恐惧视频评论更负面、愤怒和厌恶，但负面情绪并未自我强化，讨论呈现争议性而非一边倒。
正文
Abstract:TikTok has become a primary channel for nutrition information, and alongside evidence-based content, it hosts a growing stream of \emph{fear-based} wellness messaging that frames ordinary foods as ``toxic,'' ``poison,'' or things to ``never eat.'' Prior work has extensively studied political and public-health misinformation and audited the accuracy of health videos, but to the best of our knowledge, no work has examined how everyday food-fear content spreads and how audiences respond to it. To investigate this, we collected 97,234 TikTok food and nutrition videos, labeled them \textit{Fear} or \textit{Non-Fear} with a zero-shot multimodal classifier built on Gemini~3 Flash that is validated against human consensus (Cohen's $\kappa=0.81$), and analyzed more than 1.1M comments and 272K parent-reply pairs. Contrary to the intuition that fear ``goes viral,'' fear videos reach fewer people than non-fear videos. Their distinctive signature lies in dynamics: they are shared more per view (1.7--2.1$\times$ after controls) and their discussion persists longer (comment half-life 1.62$\times$ after controls). These shifts are modest in size but hold after controlling for reach. Comments on fear videos are more negative, angrier, and more disgusted, yet negativity is \emph{not} more self-reinforcing: negative comments draw negative replies at the same rate as in non-fear threads, while positive comments are far less likely to stay positive. Fear threads are contested rather than one-sided, with both more corrective and more alarm-amplifying replies. Fear discourse concentrates in frames about industrial ingredients, contaminants, and institutional distrust, and its repertoire of frames widens over 2020--2026. Our results argue for persistence-based rather than virality-based signals when platforms seek harmful health content.
| Subjects: | Social and Information Networks (cs.SI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.33898 [cs.SI] |
| (or arXiv:2610.33898v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33898 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mohit Singhal [view email]
[v1]
Sat, 24 Oct 2026 20:17:42 UTC (776 KB)
来源：arXiv cs.CY · arxiv.org