Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation

Bucos, Marian; Drăgulescu, Bogdan

doi:10.3390/app132413207

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Many researchers have primarily focused on using Natural Language Processing (NLP) techniques to detect fake text-based content [21][22][23][24], often overlooking the fact that news articles frequently include both textual and visual elements. An illustrative example can be seen in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

Zheng,

Chen,

et al. 2024

Electronics

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With the development of social media, the amount of fake news has risen significantly and had a great impact on both individuals and society. The restrictions imposed by censors make the objective reporting of news difficult. Most studies use supervised methods, relying on a large amount of labeled data for fake news detection, which hinders the effectiveness of the detection. Meanwhile, the focus of these studies is on the detection of fake news in a single modality, either text or images, but actual fake news is more often in the form of text–image pairs. In this paper, we introduce a self-supervised model grounded in contrastive learning. This model facilitates simultaneous feature extraction for both text and images by employing dot product graphic matching. Through contrastive learning, it augments the extraction capability of image features, leading to a robust visual feature extraction ability with reduced training data requirements. The model’s effectiveness was assessed against the baseline using the COSMOS fake news dataset. The experiments reveal that, when detecting fake news with mismatched text–image pairs, only approximately 3% of the data are used for training. The model achieves an accuracy of 80%, equivalent to 95% of the original model’s performance using full-size data for training. Notably, replacing the text encoding layer enhances experimental stability, providing a substantial advantage over the original model, specifically on the COSMOS dataset.

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Section: Introductionmentioning

confidence: 99%

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

Zheng,

Chen,

et al. 2024

Electronics

View full text Add to dashboard Cite

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Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches

Moisi,

Mihalca,

Coman

et al. 2024

Applied Sciences

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Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets—FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models—BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set.

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Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation

Cited by 2 publications

References 37 publications

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

Few-Shot Learning for Misinformation Detection Based on Contrastive Models

Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches

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