A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection

• URL: http://arxiv.org/abs/2304.01492v5
• Date: Tue, 17 Oct 2023 02:37:46 GMT
• Title: A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
• Authors: Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng
• Abstract summary: existing rumor detection algorithms show promising performance on yesterday's news. Due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events. We propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced with only few-shot annotations.
• Score: 11.201348902221257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced with only few-shot annotations. More specifically, we first represent rumor circulated on social media as an undirected topology for enhancing the interaction of user opinions, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm to mine effective clues simultaneously from post semantics and propagation structure. Our model explicitly breaks the barriers of the domain and/or language issues, via language alignment and a novel domain-adaptive contrastive learning mechanism. To well-generalize the representation learning using a small set of annotated target events, we reveal that rumor-indicative signal is closely correlated with the uniformity of the distribution of these events. We design a target-wise contrastive training mechanism with three event-level data augmentation strategies, capable of unifying the representations by distinguishing target events. Extensive experiments conducted on four low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

Related papers

• Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection [25.03964361177406]
  We propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information. In order to enhance the model's ability to learn the distinct patterns of rumors and non-rumors, we introduce a regularizer to further improve the model's performance.
  arXiv  Detail & Related papers  (2024-04-24T05:05:58Z)
• Out-of-distribution Rumor Detection via Test-Time Adaptation [21.342632695285364]
  We propose a simple and efficient method named Test-time Adaptation for Rumor Detection under distribution shifts (TARD) This method models the propagation of news in the form of a propagation graph, and builds propagation graph test-time adaptation framework. Experiments conducted on two group datasets collected from real-world social platforms demonstrate that our framework outperforms the state-of-the-art methods in performance.
  arXiv  Detail & Related papers  (2024-03-26T14:24:01Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets [30.315424983805087]
  This paper is in-depth evaluation of the performance gap between content and context-based models. Our empirical findings demonstrate that context-based models are still overly dependent on the information derived from the rumors' source post. Based on our experimental results, the paper also offers practical suggestions on how to minimize the effects of temporal concept drift in static datasets.
  arXiv  Detail & Related papers  (2023-09-20T18:27:19Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning [24.72097408129496]
  Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. We propose a novel framework based on prompt learning to detect rumors falling in different domains or presented in different languages. Our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
  arXiv  Detail & Related papers  (2022-12-02T12:04:48Z)
• Rumor Detection with Self-supervised Learning on Texts and Social Graph [101.94546286960642]
  We propose contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. We term this framework as Self-supervised Rumor Detection (SRD) Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
  arXiv  Detail & Related papers  (2022-04-19T12:10:03Z)
• Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning [8.013665071332388]
  We propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. Our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
  arXiv  Detail & Related papers  (2022-04-18T03:10:34Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Unsupervised Transfer Learning for Spatiotemporal Predictive Networks [90.67309545798224]
  We study how to transfer knowledge from a zoo of unsupervisedly learned models towards another network. Our motivation is that models are expected to understand complex dynamics from different sources. Our approach yields significant improvements on three benchmarks fortemporal prediction, and benefits the target even from less relevant ones.
  arXiv  Detail & Related papers  (2020-09-24T15:40:55Z)
• Stylized Adversarial Defense [105.88250594033053]
  adversarial training creates perturbation patterns and includes them in the training set to robustify the model. We propose to exploit additional information from the feature space to craft stronger adversaries. Our adversarial training approach demonstrates strong robustness compared to state-of-the-art defenses.
  arXiv  Detail & Related papers  (2020-07-29T08:38:10Z)

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.