As social media becomes a hotbed for the spread of misinformation, the
crucial task of rumor detection has witnessed promising advances fostered by
open-source benchmark datasets. Despite being widely used, we find that these
datasets suffer from spurious correlations, which are ignored by existing
studies and lead to severe overestimation of existing rumor detection
performance. The spurious correlations stem from three causes: (1) event-based
data collection and labeling schemes assign the same veracity label to multiple
highly similar posts from the same underlying event; (2) merging multiple data
sources spuriously relates source identities to veracity labels; and (3)
labeling bias. In this paper, we closely investigate three of the most popular
rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and
propose event-separated rumor detection as a solution to eliminate spurious
cues. Under the event-separated setting, we observe that the accuracy of
existing state-of-the-art models drops significantly by over 40%, becoming only
comparable to a simple neural classifier. To better address this task, we
propose Publisher Style Aggregation (PSA), a generalizable approach that
aggregates publisher posting records to learn writing style and veracity
stance. Extensive experiments demonstrate that our method outperforms existing
baselines in terms of effectiveness, efficiency and generalizability.

针对传言检测中存在的因多种原因导致的假相关性跟踪误导了已有方法，本文提出了事件分离式谣言检测方法并给出了出版者样式聚合，证明了该方法在准确性、效率和普适性方面均优于现有基线。