Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets

arXiv:2309.11576v2 Announce Type: replace
Abstract: A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that content-based (i.e., using solely source posts as input) rumor detection models tend to perform less effectively on unseen rumors. At the same time, the potential of context-based models remains largely untapped. The main contribution of this paper is in the in-depth evaluation of the performance gap between content …

abstract arxiv computational cs.cl datasets detection limitations research rumor rumors type