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New events emerge over time influencing the topics of rumors in social media.Current rumor detection benchmarks use random splits as training, developmentand test sets which typically results in topical overlaps. Consequently, modelstrained on random splits may not perform well on rumor classification onpreviously unseen topics due to the temporal concept drift. In this paper, weprovide a re-evaluation of classification models on four popular rumordetection benchmarks considering chronological instead of random splits. Ourexperimental results show that the use of random splits can significantlyoverestimate predictive performance across all datasets and models. Therefore,we suggest that rumor detection models should always be evaluated usingchronological splits for minimizing topical overlaps.

It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits