Fake news detection in Slovak language using deep learning techniques

Ivancová, Klaudia; Sarnovski, Martin; Maslej-Krcsnakova, Viera

doi:10.1109/sami50585.2021.9378650

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…The paper Ivancová et al ( 2021 ) focuses on detecting fake news in articles written in the Slovak language. A labeled dataset of political news articles published by online news portals and suspicious conspiratorial portals was created to train deep learning models.…”

Section: Current Research Analysismentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.

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Section: Current Research Analysismentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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“…A CNN is a particular type of network architecture for deep learning algorithms utilized for tasks like image recognition and pixel data processing [38] . However, it is still successful in text processing today.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Kurdish Fake News Detection Based on Machine Learning Approaches

Salh,

Nabi

2023

PJBAS

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The widespread use of social media platforms and the internet has increased information sharing, including both true and false news. Detecting fake news is challenging, and several studies have been conducted to automate this process for popular languages such as English and Arabic. However, more research must be done on detecting fake news in low-resource languages such as Kurdish. This gap was addressed, and a publicly available Kurdish fake news dataset (KDFND) was used, comprising 100962 news articles, among which 50751 are real, and 50211 are fake news labeled as Real and Fake. In this study, three techniques were implemented to extract features from news texts, including word embedding, term frequency-inverse document frequency, and count vector, and three various machine learning and deep learning classifiers were used (Random Forest, Support Vector Machine, and Convolutional Neural Networks) to identify the fake news dataset. The results showed that fake news with textual content could be identified, especially when convolutional neural networks are used. According to the experimental results of the study, CNN performs better than the other models, with an F1score of 95% and an accuracy of more than 91% percent. These findings indicate that machine learning methods can efficiently detect fake news in low-resource languages like Kurdish, even in complex environments.

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Debunking multi-lingual social media posts using deep learning

Kotiyal

Pathak

Singh

2023

Int. j. inf. tecnol.

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Fake news on social media has become a growing concern due to its potential impact on shaping public opinion. The proposed Debunking Multi-Lingual Social Media Posts using Deep Learning (DSMPD) approach offers a promising solution to detect fake news. The DSMPD approach involves creating a dataset of English and Hindi social media posts using web scraping and Natural Language Processing (NLP) techniques. This dataset is then used to train, test, and validate a deep learning-based model that extracts various features, including Embedding from Language Models (ELMo), word and n-gram counts, Term Frequency-Inverse Document Frequency (TF-IDF), sentiments, polarity, and Named Entity Recognition (NER). Based on these features, the model classifies news items into five categories: real, could be real, could be fabricated, fabricated, or dangerously fabricated. To evaluate the performance of the classifiers, the researchers used two datasets comprising over 45,000 articles. Machine learning (ML) algorithms and Deep learning (DL) model are compared to choose the best option for classification and prediction.

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Fake news detection in Slovak language using deep learning techniques

Cited by 11 publications

References 4 publications

A novel approach to fake news classification using LSTM-based deep learning models

A novel approach to fake news classification using LSTM-based deep learning models

Kurdish Fake News Detection Based on Machine Learning Approaches

Debunking multi-lingual social media posts using deep learning

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