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} }
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 theā¦
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