Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection

@article{Han2019NeuralLM,
  title={Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection},
  author={Sooji Han and Jie Gao and Fabio Ciravegna},
  journal={2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  year={2019},
  pages={105-112}
}
  • Sooji Han, Jie Gao, F. Ciravegna
  • Published 16 July 2019
  • Computer Science
  • 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
The scarcity and class imbalance of training data are known issues in current rumor detection tasks. [] Key Method 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…
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