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Adapting Pre-trained Language Models to Rumor Detection on Twitter
2021
Journal of universal computer science (Online)
Fake news has invaded social media platforms where false information is being propagated with malicious intent at a fast pace. These circumstances required the development of solutions to monitor and detect rumor in a timely manner. In this paper, we propose an approach that seeks to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model to the task of rumor detection, namely RoBERTa. A comparison against content-based characteristics has shown the capability of
doi:10.3897/jucs.65918
fatcat:bdtfurxsfjastmqsrffdvigafm