Attention-based C-BiLSTM for fake news detection

Trueman, Tina Esther; Kumar, Jay; Narayanasamy, P.; Vidya, J

doi:10.1016/j.asoc.2021.107600

Cited by 55 publications

(15 citation statements)

References 10 publications

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“…In terms of features extraction, fake news detection approaches can be categorized into four groups: knowledge-based [11,23,44,45], content-based [6,13,23,46], user-based [33,47,48], and propagation-based [1,43,48]. The knowledge-based includes fact-checking and crowdsourcing.…”

Section: Related Workmentioning

confidence: 99%

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

et al. 2023

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Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

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Section: Related Workmentioning

confidence: 99%

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

et al. 2023

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“…For text classification tasks, the attention mechanism is very useful to focus on parts or words of the text that are more inclined towards deciding the label of the text. [53][54][55] We used the Keras library to implement the attention mechanism with different deep-learning models.…”

Section: Attention In Text Classificationmentioning

confidence: 99%

“…This same analogy is used in deep learning tasks to concentrate on the task's more inclined features and ignore irrelevant ones. For text classification tasks, the attention mechanism is very useful to focus on parts or words of the text that are more inclined towards deciding the label of the text 53‐55 . We used the Keras library to implement the attention mechanism with different deep‐learning models.…”

Section: And DL Models Used For Comparisonmentioning

confidence: 99%

An attention‐based deep learning model for credibility assessment of online health information

Padhy

Rathore

2023

Computational Intelligence

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With the surge of searching and reading online health‐based articles, maintaining the quality and credibility of online health‐based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health‐related articles' credibility. The paper proposed an Attention‐based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1‐score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short‐term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN‐CNN. The experimental results showed that the proposed model produced state‐of‐the‐art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1‐score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.

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“…It is di cult to distinguish false news since the terminology used in reports is remarkably comparable to those used in actual news. [11] As a result of faulty message identi cation, 1.2. Challenges:…”

Section: The Study's Motivationmentioning

confidence: 99%

Fake News Detection in Different Applications: State-of-the-art and Future Directions

Jolly

Kumar

2022

Preprint

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Social media platforms such as Telegram, Twitter, WhatsApp, and Facebook have grown indispensable for information exchange and delivery in recent years. Humans have a tendency to trust any piece of content that circulates on social media without questioning its authenticity or origin. Fascinated people sometimes spread fake news on social media because of its convenience for information sharing. Additionally, it is used for personal advantages, such as amending government policies and defaming eminent personalities. Hence, to avoid the fatal outcome of spreading fake news, different methods are developed for its identification that achieves high accuracy. This paper reviews conventional artificial news detection methods to solve the mentioned issue. The Systematic Literature Review, often known as SLR, is a procedure that assesses review papers connected to detecting fake news. A technique known as unsupervised learning was used for this article review, along with ensemble learning, semi-supervised learning, reinforcement learning, and supervised learning. This methodology's effectiveness is evaluated regarding the precision of the measurements taken. In conclusion, the deep learning model generates practical outputs and outperforms classical machine learning models.

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Attention-based C-BiLSTM for fake news detection

Cited by 55 publications

References 10 publications

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

An attention‐based deep learning model for credibility assessment of online health information

Fake News Detection in Different Applications: State-of-the-art and Future Directions

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