Noisy Text Data: Achilles' Heel of BERT

Kumar, Ankit; Makhija, Piyush; Gupta, Anuj

doi:10.48550/arxiv.2003.12932

Cited by 2 publications

(1 citation statement)

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“…Twitter data contains a significant amount of noise, which can significantly hinder the performance of Transformer models [32,33]. Preprocessing can alleviate the problem by standardizing the input data.…”

Section: Data Preprocessingmentioning

confidence: 99%

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

Patel,

Bansal,

Kaur

2023

Preprint

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Social media has become a significant source of essential facts and alarming falsehoods, including rumours. A significant increase in rumour spreading has occurred due to the lack of an autonomous rumour detection mechanism, causing widespread and severe social repercussions. To address this challenge, we present a ground-breaking method for developing an automatic rumour detection system, focusing on the fundamental problem of class imbalance in rumour detection. Our method selectively uses oversampling to obtain a uniformly distributed dataset by leveraging contextualised data augmentation techniques to generate synthetic samples for underrepresented classes. Additionally, we effectively recreate non-linear dialogues inside a thread using two novel graph neural networks (GNNs), which improves the system's capacity to understand complex links between postings. Our method employs a distinctive feature selection mechanism to enhance further Twitter representations based on the state-of-the-art BERTweet model. The thorough analysis of our methodology using three publicly accessible datasets yielded compelling results: 1) Our GNN models outperformed the most state-of-the-art classifiers in F1-score by more than 20%. Emphasizing the importance of our approach to developing sophisticated rumour detection systems. 2) By utilizing our oversampling method, we significantly improve the F1-score by 9%, highlighting the practical implications of resolving class imbalance. 3) Our technique delivers further performance increases through non-random selection criteria for data augmentation, with the selection of relevant tweets highlighting the significance of our novel augmentation strategy. 4) Notably, our approach captures rumours in their early stages more effectively than previous classifiers, establishing a baseline for future works. The innovative aspects of our proposed method lie in its ability to solve class imbalance effectively, outperform existing classifiers in terms of performance, and drastically reduce the propagation of rumours and false information on social media platforms. Our study lays the way for developments in rumour detection by offering a comprehensive solution, eventually helping to ensure the veracity of information flowing online. We are confident that our findings have an influence on the broader field of rumour detection systems and provide fresh directions for further study.

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Section: Data Preprocessingmentioning

confidence: 99%