Neural language model based training data augmentation for weakly supervised early rumor detection

Han, S., Gao, J. orcid.org/0000-0002-3610-8748 and Ciravegna, F. (2019) Neural language model based training data augmentation for weakly supervised early rumor detection. In: Proceedings of 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2019 IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2019), 27-30 Aug 2019, Vancouver, Canada. ACM , pp. 105-112. ISBN 9781450368681

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Copyright, Publisher and Additional Information: © 2019 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Data augmentation; weak supervision; rumor detection; social media
Dates:
  • Accepted: 20 June 2019
  • Published (online): 27 August 2019
  • Published: 27 August 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 20 Jun 2019 09:57
Last Modified: 15 Jul 2020 12:38
Status: Published
Publisher: ACM
Refereed: Yes
Identification Number: https://doi.org/10.1145/3341161.3342892
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