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.

翻译：关于虚假信息的研究在很大程度上依赖于虚假新闻检测，这一任务被定义为对文章或声明所附真实性标签的预测。然而，社会科学研究反复强调，信息操纵不仅限于捏造内容，往往还依赖于放大传播的动态机制。这一理论转向对应用社会科学研究的操作化具有重要影响。当预测目标从真实性转向传播性时，实证层面会发生哪些变化？在资源有限的情况下又能达到何种性能水平？本文基于EVONS和FakeNewsNet两个数据集，对虚假新闻检测与病毒式传播预测进行了比较研究。我们采用评估优先的视角，考察当预测目标从真实性转向传播性时，基准模型的行为如何变化。实验表明，虚假新闻检测在获得强文本嵌入后相对稳定，而病毒式传播预测对阈值定义和早期观察窗口等操作选择则敏感得多。本文提出了操作性强的轻量级透明流程构建方法，用于虚假信息相关预测任务，其性能可与前沿技术相媲美。