Learning Domain-Invariant Features for Out-of-Context News Detection

• URL: http://arxiv.org/abs/2406.07430v2
• Date: Thu, 8 Aug 2024 07:34:50 GMT
• Title: Learning Domain-Invariant Features for Out-of-Context News Detection
• Authors: Yimeng Gu, Mengqi Zhang, Ignacio Castro, Shu Wu, Gareth Tyson,
• Abstract summary: Out-of-context news is a common type of misinformation on online media platforms. In this work, we focus on domain adaptive out-of-context news detection. We propose ConDA-TTA which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features.
• Score: 19.335065976085982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Existing out-of-context news detection models only consider the scenario where pre-labeled data is available for each domain, failing to address the out-of-context news detection on unlabeled domains (e.g. news topics or agencies). In this work, we therefore focus on domain adaptive out-of-context news detection. In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features. In addition, we leverage test-time target domain statistics to further assist domain adaptation. Experimental results show that our approach outperforms baselines in most domain adaptation settings on two public datasets, by as much as 2.93% in F1 and 2.08% in accuracy.

Related papers

• DPOD: Domain-Specific Prompt Tuning for Multimodal Fake News Detection [15.599951180606947]
  Fake news using out-of-context images has become widespread and is a relevant problem in this era of information overload. We explore whether out-of-domain data can help to improve out-of-context misinformation detection of a desired domain. We propose a novel framework termed DPOD (Domain-specific Prompt-tuning using Out-of-Domain data)
  arXiv  Detail & Related papers  (2023-11-27T08:49:26Z)
• Robust Domain Misinformation Detection via Multi-modal Feature Alignment [49.89164555394584]
  We propose a robust domain and cross-modal approach for multi-modal misinformation detection. It reduces the domain shift by aligning the joint distribution of textual and visual modalities. We also propose a framework that simultaneously considers application scenarios of domain generalization.
  arXiv  Detail & Related papers  (2023-11-24T07:06:16Z)
• A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification [46.47734465505251]
  Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. We propose a two-stage framework for cross-domain text classification.
  arXiv  Detail & Related papers  (2023-04-18T06:21:40Z)
• Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer [16.886024206337257]
  We propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND) DITFEND could improve the performance of specific target domains. Online experiments show that it brings additional improvements over the base models in a real-world scenario.
  arXiv  Detail & Related papers  (2022-09-19T10:21:13Z)
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives [16.68091981866261]
  Unsupervised domain adaptation (UDA) is proposed to counter the performance drop on data in a target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc.
  arXiv  Detail & Related papers  (2022-08-15T20:05:07Z)
• Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation [88.5448806952394]
  We consider unsupervised domain adaptation (UDA), where labeled data from a source domain and unlabeled data from a target domain are used to learn a classifier for the target domain. We show that contrastive pre-training, which learns features on unlabeled source and target data and then fine-tunes on labeled source data, is competitive with strong UDA methods.
  arXiv  Detail & Related papers  (2022-04-01T16:56:26Z)
• Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training [67.71228426496013]
  We show that using target domain data during pre-training leads to large performance improvements across a variety of setups. We find that pre-training on multiple domains improves performance generalization on domains not seen during training.
  arXiv  Detail & Related papers  (2021-04-02T12:53:15Z)
• Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation [76.41664929948607]
  Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks. We present a novel and effective method to tackle this problem by using effective inter-domain and intra-domain semantic information propagation. Our source code and pre-trained models will be released soon.
  arXiv  Detail & Related papers  (2020-12-04T14:28:19Z)
• Domain Adaptation for Semantic Parsing [68.81787666086554]
  We propose a novel semantic for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies.
  arXiv  Detail & Related papers  (2020-06-23T14:47:41Z)
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation [65.38975706997088]
  Open set domain adaptation (OSDA) assumes the presence of unknown classes in the target domain. We show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps. We propose a novel framework to specifically address the larger domain gaps.
  arXiv  Detail & Related papers  (2020-03-08T14:20:24Z)

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.