  Rumors are often associated with newly emerging events, thus, an ability to
deal with unseen rumors is crucial for a rumor veracity classification model.
Previous works address this issue by improving the model's generalizability,
with an assumption that the model will stay unchanged even after the new
outbreak of an event. In this work, we propose an alternative solution to
continuously update the model in accordance with the dynamics of rumor domain
creations. The biggest technical challenge associated with this new approach is
the catastrophic forgetting of previous learnings due to new learnings. We
adopt continual learning strategies that control the new learnings to avoid
catastrophic forgetting and propose an additional strategy that can jointly be
used to strengthen the forgetting alleviation.
