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
Abstract
The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-focused Twitter corpora are employed to learn context-sensitive representations of rumor tweets. Six different real-world events based on three publicly available rumor datasets are employed in our experiments to provide a comparative evaluation of the effectiveness of the method. The results show that our method can expand the size of an existing rumor data set nearly by 200% and corresponding social context (i.e., conversational threads) by 100% with reasonable quality. Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs). With augmented data, the performance of rumor detection can be improved by 12.1% in terms of F-score. Our experiments also indicate that augmented training data can help to generalize rumor detection models on unseen rumors.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
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: |
|
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: | 10.1145/3341161.3342892 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147610 |