From Fake to Hyperpartisan News Detection Using Domain Adaptation
Not on the map. Why?
Abstract (arXiv)
Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and cross-domain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 69 references that are in the map. First posted on arXiv 4 Aug 2023.
Builds on
32 of 69 references.
References · 32
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2015Domain-Adversarial Training of Neural Networkscited by 3,044
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2019The Curious Case of Neural Text Degenerationcited by 2,916
- 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
20 more
- 2018Hierarchical Neural Story Generationcited by 1,193
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
- 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
- 2018Universal Sentence Encodercited by 489
- 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
- 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 326
- 2019Knowledge Enhanced Contextual Word Representationscited by 296
- 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 285
- 2017Beam Search Strategies for Neural Machine Translationcited by 232
- 2019Not Enough Data? Deep Learning to the Rescue!cited by 175
- 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
- 2017Automatic Detection of Fake Newscited by 145
- 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 118
- 2017Beyond News Contents: The Role of Social Context for Fake News Detectioncited by 91
- 2019Cluster Alignment with a Teacher for Unsupervised Domain Adaptationcited by 88
- 2017A Stylometric Inquiry into Hyperpartisan and Fake Newscited by 82
- 2013Probabilistic Latent Semantic Analysiscited by 63
- 2022Domain Adaptive Fake News Detection via Reinforcement Learningcited by 27
- 2021Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensemblescited by 14
- 2020Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-casecited by 12
Built on by
Citing papers per year: 2024: 1.
1 paper.