mlsanityData to 7 Oct 2026

A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model

arXiv 2202.01907 ↗First posted 3 Feb 2022cs.LGcited by 0

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Abstract (arXiv)

An efficient fake news detector becomes essential as the accessibility of social media platforms increases rapidly.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 60 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

17 of 60 references.

References · 17

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  3. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  4. 2019Why gradient clipping accelerates training: A theoretical justification for adaptivitycited by 335
  5. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  6. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  7. 2017Automatic Detection of Fake Newscited by 145
  8. 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 140
  9. 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 115
  10. 2020Hyper-Parameter Optimization: A Review of Algorithms and Applicationscited by 114
  11. 2017CSI: A Hybrid Deep Model for Fake News Detectioncited by 101
  12. 2019Fake News Early Detection: An Interdisciplinary Studycited by 29
5 more
  1. 2018A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunitiescited by 28
  2. 2019Evaluating Language Model Finetuning Techniques for Low-resource Languagescited by 12
  3. 2020Detecting Fake News with Capsule Neural Networkscited by 8
  4. 2021Transforming Fake News: Robust Generalisable News Classification Using Transformerscited by 8
  5. 2021Differentially Private Federated Learning for Cancer Predictioncited by 6

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