Enhancing Fake News Detection in Low-Resource Linguistic Contexts using Translation-based NER and Lightweight NLI

Enhancing Fake News Detection using a BERT–BiLSTM Hybrid Framework

The rapid proliferation of digitally distributed news has made large-scale automated verification an urgent research priority, as deceptive content spreads across social platforms faster than manual reviewers can evaluate it. This work introduces a two-stage deep learning pipeline in which a pretrained BERT encoder generates rich contextual token embeddings that are subsequently processed by a Bidirectional Long Short-Term Memory (BiLSTM) network, enabling the classifier to exploit both sentence-level semantics and document-level narrative flow simultaneously. Experiments on the WELFake benchmark [1] following a standardised cleaning pipeline—lowercasing, URL stripping, tokenisation, stop-word removal, and WordNet lemmatisation— yielded 98.7 % accuracy and an F1-score of 0.986. This surpasses a BERT-only baseline by 3.1 percentage points and exceeds several previously published state-of-the-art results. The gains confirm that coupling transformer-based semantic representations with recurrent sequential modelling produces a measurable and reproducible improvement in misinformation detection. Planned extensions include multilingual evaluation and knowledge-distilled encoder replacements targeting real-time throughput constraints.

FakeDiverse a curated multi-source news corpus for context-aware fake news detection using BERT and DeBERTa

This study examines the effectiveness of two transformer-based architectures—BERT and DeBERTa—for identifying fake news using only textual information from headlines and article bodies and achieves strong performance on FakeDiverse corpus, demonstrating the need for enhanced generalization strategies as well as domain adaptation.

Multilingual Fake News Detection Using Machine Learning with Contextual-Based Feature Extraction

Fake news has become a major challenge in today’s digital environment, particularly in languages where labeled data is limited. Most existing research has primarily focused on English due to the easy availability of annotated datasets, whereas low-resource languages such as Bengali remain underexplored. This study presents a multilingual approach for fake news detection using machine learning with contextual-based feature extraction. The proposed method integrates n-gram techniques with sentence-level contextual embeddings to capture both word-level patterns and semantic meaning. Since labeled data is not available for the Bengali dataset, a translation-based strategy is employed, followed by a pseudo-labeling process to assign labels automatically. The models are trained on English news titles and subsequently evaluated on both English and Bengali datasets to examine their cross-lingual effectiveness. The experimental findings indicate that ensemble-based classifiers such as Random Forest and Gradient Boosting achieve reliable performance across both languages. In some cases, the results for Bengali data are comparable or slightly better than those for English. The study demonstrates that effective fake news detection is possible in low-resource languages using short text data without relying on manually labeled datasets. The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments.

Cross-lingual transfer learning for fake news detection: leveraging high-resource language for low-resource adaptation

Improved Sequence Labeling Algorithms and Their Applications for Translation Error Detection

This paper proposes an alignment-aware multi-granularity tagging framework. First, this method uses a cross-lingual pre-trained model to encode source and target language contexts jointly while explicitly modeling-level bilingual correspondences via a learnable soft alignment layer. Second, a gated local enhancement module is introduced to dynamically fuse n-gram-level surface features on top of the Transformer high-level representation, preserving fine-grained error signals that are easily smoothed by attention mechanisms. Finally, a label-aware focus loss function is designed to alleviate the extreme imbalance between positive and negative samples. This function supports joint prediction of four types of fine-grained error labels. Results showed that on the large-scale benchmark of WMT QE Shared Task 2023, covering six language pairs, alignment-aware multi-granularity tagging achieved 78.6% in Micro-F1, 72.3% in Macro-F1, and improved Recall@Rare to 62.3%, especially excelling in low-frequency error types and significantly outperforming the baseline model.

An academic English text ambiguity detection algorithm based on contextual word embeddings and deep learning

Contextual word embeddings and deep learning architectures have demonstrated remarkable potential in advancing ambiguity detection for academic English texts. This study introduces a lightweight yet highly accurate sequence labeling algorithm that combines domain-adaptive contextual embeddings with a BiLSTM-CRF framework augmented by an attention mechanism. This study addresses the pressing challenge of accurately identifying lexical and syntactic ambiguities in heterogeneous academic corpora. Traditional rule-based and static embedding methods have been unable to meet this requirement. To ensure language diversity and robustness, the corpus from ACL Anthology and IEEE Xplore was comprehensively preprocessed and fine-tuned in a specific field. Experimental evaluation shows that the proposed model achieves state-of-the-art accuracy while maintaining the computational efficiency required for real-time applications. The proposed model significantly outperforms the traditional baselines in terms of both recall and precision. Using model quantization techniques can greatly reduce inference time and detection performance. Contextual representation and deep sequence modeling can achieve reliable and scalable ambiguity detection in challenging academic fields. This suggests that this can provide concrete solutions for machine translation and academic search systems.