Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique Article Swipe
Abdullah Ali
,
Fuad A. Ghaleb
,
Bander Ali Saleh Al‐rimy
,
Fawaz Alsolami
,
Asif Irshad Khan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22186970
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22186970
Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22186970
- https://www.mdpi.com/1424-8220/22/18/6970/pdf?version=1663215145
- OA Status
- gold
- Cited By
- 48
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295927380
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295927380Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22186970Digital Object Identifier
- Title
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Deep Ensemble Fake News Detection Model Using Sequential Deep Learning TechniqueWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-15Full publication date if available
- Authors
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Abdullah Ali, Fuad A. Ghaleb, Bander Ali Saleh Al‐rimy, Fawaz Alsolami, Asif Irshad KhanList of authors in order
- Landing page
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https://doi.org/10.3390/s22186970Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/22/18/6970/pdf?version=1663215145Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/22/18/6970/pdf?version=1663215145Direct OA link when available
- Concepts
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Deep learning, Fake news, Computer science, Artificial intelligence, Ensemble learning, Machine learning, Internet privacyTop concepts (fields/topics) attached by OpenAlex
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48Total citation count in OpenAlex
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2025: 10, 2024: 22, 2023: 14, 2022: 2Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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