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

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-ofthe-art neural language model (NLM) and large credibilityfocused 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.

Rumour detection using Graph Neural Network and oversampling in the benchmark Twitter datasets

“…The augmented dataset was used to train a DNN model for rumour detection using DNN model. [48] RDPNN: A hybrid deep learning model was trained to learn individual tweet-level attributes, user responses, and tweet-level metadata. The model was trained using the augmented PHEME5 dataset.…”

Adapting Pre-trained Language Models to Rumor Detection on Twitter

“…In the context of rumor detection, the use of PLMs is still in its early investigations. To the best of our knowledge, only a work by [Han et al, 2019] has explored ELMo PLM [Peters et al, 2018] to augment rumor datasets and improve the performance of rumor detection models. Furthermore, approaches seeking to detect rumor or fake news in other platforms (Reddit, News Outlets, Facebook) are not within the scope of our study [Jwa et al, 2019, Majumder and Das, 2020, Baruah et al, 2020.…”