Rumour detection using graph neural network and oversampling in benchmark Twitter dataset

• URL: http://arxiv.org/abs/2212.10080v1
• Date: Tue, 20 Dec 2022 08:43:10 GMT
• Title: Rumour detection using graph neural network and oversampling in benchmark Twitter dataset
• Authors: Shaswat Patel, Prince Bansal, Preeti Kaur
• Abstract summary: We propose a novel method for building automatic rumour detection system by focusing on oversampling. Our oversampling method relies on contextualised data augmentation to generate synthetic samples for underrepresented classes in the dataset. Two graph neural networks(GNN) are proposed to model non-linear conversations on a thread.
• Score: 0.30079490585515345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, online social media has become a primary source for new information and misinformation or rumours. In the absence of an automatic rumour detection system the propagation of rumours has increased manifold leading to serious societal damages. In this work, we propose a novel method for building automatic rumour detection system by focusing on oversampling to alleviating the fundamental challenges of class imbalance in rumour detection task. Our oversampling method relies on contextualised data augmentation to generate synthetic samples for underrepresented classes in the dataset. The key idea exploits selection of tweets in a thread for augmentation which can be achieved by introducing a non-random selection criteria to focus the augmentation process on relevant tweets. Furthermore, we propose two graph neural networks(GNN) to model non-linear conversations on a thread. To enhance the tweet representations in our method we employed a custom feature selection technique based on state-of-the-art BERTweet model. Experiments of three publicly available datasets confirm that 1) our GNN models outperform the the current state-of-the-art classifiers by more than 20%(F1-score); 2) our oversampling technique increases the model performance by more than 9%;(F1-score) 3) focusing on relevant tweets for data augmentation via non-random selection criteria can further improve the results; and 4) our method has superior capabilities to detect rumours at very early stage.

Related papers

• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score-Softmax Classifier [7.302402275736439]
  Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely prediction of distractions, fatigue, and potential hazards. Recent research has exposed unreliable cross-dataset end-to-end driver behavior recognition due to overfitting. We introduce the Score-Softmax classifier, which addresses this issue by enhancing inter-class independence and Intra-class uncertainty.
  arXiv  Detail & Related papers  (2023-10-08T15:28:01Z)
• Neural Priming for Sample-Efficient Adaptation [92.14357804106787]
  We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks. Neural Priming can be performed at test time, even for pretraining as large as LAION-2B.
  arXiv  Detail & Related papers  (2023-06-16T21:53:16Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• On Automatic Data Augmentation for 3D Point Cloud Classification [19.338266486983176]
  We propose to automatically learn a data augmentation strategy using bilevel optimization. An augmentor is designed in a similar fashion to a conditional generator and is optimized by minimizing a base model's loss on a validation set. We evaluate our approach on standard point cloud classification tasks and a more challenging setting with pose misalignment between training and validation/test sets.
  arXiv  Detail & Related papers  (2021-12-11T17:14:16Z)
• Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data [0.0]
  Time-series data are one of the fundamental types of raw data representation used in data-driven techniques. Deep Neural Networks (DNNs) require huge labeled training samples to reach their optimum performance. In this study, a data augmentation technique named ensemble augmentation is proposed to overcome this limitation.
  arXiv  Detail & Related papers  (2021-08-06T20:04:29Z)
• SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection [9.924083358178239]
  We propose two variants of self-attention for contextual modeling in 3D object detection. We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors. Next, we propose a self-attention variant that samples a subset of the most representative features by learning deformations over randomly sampled locations.
  arXiv  Detail & Related papers  (2021-01-07T18:30:32Z)
• A Two-Stage Approach to Device-Robust Acoustic Scene Classification [63.98724740606457]
  Two-stage system based on fully convolutional neural networks (CNNs) is proposed to improve device robustness. Our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set. Neural saliency analysis with class activation mapping gives new insights on the patterns learnt by our models.
  arXiv  Detail & Related papers  (2020-11-03T03:27:18Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.