从假新闻检测转向信息扩散预测，揭示了操作难题与应对策略。
From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders
- 对比真假新闻检测与传播预测，发现扩散预测更依赖操作细节。
- 在有限资源下，轻量级管道仍可达到顶尖模型性能。
- 提出透明可复现的轻量级方案，适合实际应用部署。
大量虚假信息研究依赖于假新闻检测任务，即对文章或声明进行真伪标签预测。然而，社会科学反复指出，信息操纵远超虚构内容，常通过传播机制实现。这一理论转向对应用社会科学研究的操作化提出挑战。当预测目标从真伪转向传播性时，实证结果有何变化？在资源受限条件下能达到何种性能？本文在EVONS和FakeNewsNet两个数据集上比较假新闻检测与传播性预测。采用评估优先视角，考察预测目标转变时基准行为的变化。实验表明，一旦具备强文本嵌入，假新闻检测表现稳定；而传播性预测则对阈值设定、早期观察窗口等操作选择极为敏感。论文提出轻量、透明的实用化流程，可在有限资源下实现与前沿模型相当的性能。
原文摘要 · Abstract (English)
A wide part of research on misinformation has relied lies on fake-news detection, a task framed as the prediction of veracity labels attached to articles or claims. Yet social-science research has repeatedly emphasized that information manipulation goes beyond fabricated content and often relies on amplification dynamics. This theoretical turn has consequences for operationalization in applied social science research. What changes empirically when prediction targets move from veracity to diffusion? And which performance level can be attained in limited resources setups ? In this paper we compare fake-news detection and virality prediction across two datasets, EVONS and FakeNewsNet. We adopt an evaluation-first perspective and examine how benchmark behavior changes when the prediction target shifts from veracity to diffusion. Our experiments show that fake-news detection is comparatively stable once strong textual embeddings are available, whereas virality prediction is much more sensitive to operational choices such as threshold definition and early observation windows. The paper proposes practical ways to operationalize lightweight, transparent pipelines for misinformation-related prediction tasks that can rival with state-of-the-art.
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