Leveraging User Comments in Tweets for Rumor Detection

Patel, Shaswat; Shah, Binil; Kaur, Preeti

doi:10.1007/978-981-16-2597-8_8

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…This definition is consistent with the majority of the works in the literature. [6][7][8][9] However, multiple works have considered rumours as false information. [10,11] In our paper, a rumour is a piece of information that is unverified or not vetted at the time of reporting.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Patel,

Bansal,

Kaur

2023

Preprint

Self Cite

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Rumour detection on benchmark twitter datasets using graph neural networks with data augmentation

Patel,

Bansal,

Kaur

2024

Soc. Netw. Anal. Min.

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Gender Recognition of Bangla Names Using Deep Learning Approaches

et al. 2022

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The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows.

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Leveraging User Comments in Tweets for Rumor Detection

Cited by 3 publications

References 10 publications

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

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

Rumour detection on benchmark twitter datasets using graph neural networks with data augmentation

Gender Recognition of Bangla Names Using Deep Learning Approaches

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