BBC-FND: An ensemble of deep learning framework for textual fake news detection

Palani, Balasubramanian; Sivasankar, E.

doi:10.1016/j.compeleceng.2023.108866

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…They are represented by text document formats such as txt, doc, pdf etc. Some of the deep learning methods which rely only on text for fake news and harmful languages include [58], [71], [89], [92] and [50], [74], [85], [112] respectively.…”

Section: A Textmentioning

confidence: 99%

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Festus Ayetiran,

Özgöbek

2024

IEEE Access

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The detection of fake news and harmful languages has become increasingly important in today's digital age. As the prevalence of fake news and harmful languages continue to increase, so also is the correspondent negative impact on individuals and the society. Researchers are exploring new techniques to identify and combat these issues. Deep neural network (DNN) has found a wide range of applications in diverse problem domains including but not limited to fake news and harmful languages detection. Fake news and harmful languages are currently increasing online and the mode of dissemination of these contents is fast changing from the traditional unimodal to multiple data forms including texts, audios, images and videos. Multimedia contents containing fake news and harmful languages pose more complex challenges than unimodal contents. The choice and efficacy of the fusion methods of the multimedia contents is one of the most challenging. Our area of focus is multimodal techniques based on deep learning that combines diverse data forms to improve detection accuracy. In this review, we delve into the current state of research, the evolution of deep learning techniques that have been proposed for multimodal fake news and harmful languages detection and the state-of-the-art (SOTA) multimedia data fusion methods. In all cases, we discuss the prospects, relationships, breakthroughs and challenges.

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Section: A Textmentioning

confidence: 99%

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Festus Ayetiran,

Özgöbek

2024

IEEE Access

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“…It leverages the distill-Bert for embeddings, the convolution neural network for extracting spatial data, the bidirectional gated recurrent unit (BiGRU) for extracting contextual data, and the self-attention-capable CapsNet for hierarchical comprehension of both complete and partial relations among data. Palani and Elango (2023) proposed the content-based ensemble of a deep learning-based framework, named the BERT-BiLSTM-convolutional neural network (CNN) for Fake News Detection (BBC-FND). Its performance was evaluated using four benchmark datasets for fake news: McIntire, Covid-19, Kaggle, and WELFake.…”

Section: Previous Work On Automatic Fake News Detectionmentioning

confidence: 99%

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

Atchariyachanvanich,

Saengkhunthod,

Kerdnoonwong

et al. 2024

Journal of Cases on Information Technology

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These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

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Automatic Fake News Detection on Social Networks using Softsign Activation based Long-Short Term Memory

John,

Al-Attabi

et al. 2024

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)

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BBC-FND: An ensemble of deep learning framework for textual fake news detection

Cited by 5 publications

References 17 publications

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

Automatic Fake News Detection on Social Networks using Softsign Activation based Long-Short Term Memory

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