The Use of Data Augmentation as a Technique for Improving Fake News Detection in the Romanian Language

Tucudean, Georgiana; Bucos, Marian

doi:10.1109/isetc56213.2022.10010213

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The Factual.ro platform provides a valuable resource for implementing a fake news detection system in the Romanian language using machine learning techniques. As we found from our discussion with the representatives of the Factual.ro platform, this resource is not available in the form of a data set, which led us to propose the implementation of our first version of the Factual.ro data set in [31]. In this paper, we extend the initial data set by extracting all available statements from the Factual.ro platform between 2014 and 2023.…”

Section: Data Collectionmentioning

confidence: 99%

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

Bucos

Tucudean

2023

Applied Sciences

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This paper aims to investigate the use of a Romanian data source, different classifiers, and text data augmentation techniques to implement a fake news detection system. The paper focusses on text data augmentation techniques to improve the efficiency of fake news detection tasks. This study provides two approaches for fake news detection based on content and context features found in the Factual.ro data set. For this purpose, we implemented two data augmentation techniques, Back Translation (BT) and Easy Data Augmentation (EDA), to improve the performance of the models. The results indicate that the implementation of the BT and EDA techniques successfully improved the performance of the classifiers used in our study. The results of our content-based approach show that an Extra Trees Classifier model is the most effective, whether data augmentation is used or not, as it produced the highest accuracy, precision, F1 score, and Kappa. The Random Forest Classifier with BT yielded the best results of the context-based experiment overall, with the highest accuracy, recall, F1 score, and Kappa. Furthermore, we found that BT and EDA led to an increase in the AUC scores of all models in both content-based and context-based data sets.

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Section: Data Collectionmentioning

confidence: 99%

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

Bucos

Tucudean

2023

Applied Sciences

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“…The experiments were carried out using the Factual.ro fake news data set, which we implemented and described in [10,23]. The data included both fake and real news instances extracted from Factual.ro, a platform that verifies shared news and content using independent experts.…”

Section: Data Setmentioning

confidence: 99%

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

Bucos,

Drăgulescu

2023

Applied Sciences

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Misinformation poses a significant challenge in the digital age, requiring robust methods to detect fake news. This study investigates the effectiveness of using Back Translation (BT) augmentation, specifically transformer-based models, to improve fake news detection in Romanian. Using a data set extracted from Factual.ro, the research finds that BT-augmented models show better accuracy, precision, recall, F1 score, and AUC compared to those using the original data set. Additionally, using mBART for BT augmentation with French as a target language improved the model’s performance compared to Google Translate. The Extra Trees Classifier and the Random Forest Classifier performed the best among the models tested. The findings suggest that the use of BT augmentation with transformer-based models, such as mBART, has the potential to enhance fake news detection. More research is needed to evaluate the effects in other languages.

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The Use of Data Augmentation as a Technique for Improving Fake News Detection in the Romanian Language

Cited by 2 publications

References 10 publications

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

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

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