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

## TL;DR

This paper introduces a neural language model-based data augmentation method that significantly increases training data for early rumor detection, improving model performance and generalization on social media rumor datasets.

## Contribution

The work presents a novel, general-purpose data augmentation technique leveraging unlabeled social media data and neural language models to enhance early rumor detection performance.

## Key findings

- Augmented data increases rumor dataset size by nearly 200%.
- Improved rumor detection F-score by 12.1%.
- Augmented data helps models generalize to unseen rumors.

## 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.