Clickbait Detection Using Deep Recurrent Neural Network

Razaque, Abdul; Alotaibi, Bandar; Alotaibi, Munif; Hussain, Shujaat; Alotaibi, Aziz; Jotsov, Vladimir

doi:10.3390/app12010504

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…Naeem et al (2020) considered 16,000 headlines for clickbait and non-clickbait headlines and got an accuracy of 0.97 for LSTM with 300 dimensional word2vec embedding and 0.88 for part of speech analysis module (POSAM). Razaque et al (2022) proposed a deep recurrent neural network (RNN) using source rating analysis that examined 1800 legal websites with an accuracy of 0.9983. Ma et al (2022) created 6,000 clickbait and 6,000 non-clickbait from Chinese news websites, and using CNN-LSTM with title embedding, content embedding, and adding 18 manual features, obtained an accuracy of 98.42%.…”

Section: Related Workmentioning

confidence: 99%

Identification of clickbait news articles using SBERT and correlation matrix

Supriya,

Singh,

Kumar

2023

Preprint

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Clickbait refers to the practice of using attention-grabbing or misleading headlines to attract readers to click on particular headlines or pieces of content. This technique often involves exaggerating claims or adding false information to attract traffic. In general, clickbait headlines are less similar to detailed articles associated with that headline compared to the standard news post. In our proposed system, we have exploited that to create features from headlines and its paragraph by using SBERT. Since the size of the paragraph is quite large compared to the headlines, we have selected a few sentences from the paragraph using a dissimilarity matrix. The extracted features from the headlines, target title and selected sentences of the paragraph are concatenated and classified using machine learning (ML) classifiers. The proposed model was tested extensively on two real-world datasets, and the results showed that it performed better than the current state-of-the-art models. The experimental results, the support vector machine (SVM) classifier with the concatenated embedding of six dissimilar sentences from paragraph exhibited the best performance with an accuracy of 0.84, weighted precision of 0.83, weighted recall of 0.84, and weighted F1-score of 0.82 surpassing the state-of-the-art model by 0.088 in terms of F1-score.

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Section: Related Workmentioning

confidence: 99%

Identification of clickbait news articles using SBERT and correlation matrix

Supriya,

Singh,

Kumar

2023

Preprint

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“…Razaque et al proposed a RNN model to determine if a link (URL) in a clickbait message is malicious or harmless [42]. Another RNN model by the same authors determines if the content pointed to by a link (e.g., an article or a social media post) is malicious or harmless [43].…”

Section: B Existing Work On Clickbait Detectionmentioning

confidence: 99%

An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection

Feng

Nguyen

2022

IEEE Open J. Comput. Soc.

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Clickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. In this article, we propose a novel attention-based neural network for the task of clickbait detection. To the best of our knowledge, our work is the first that incorporates human semantic knowledge into an artificial neural network, and uses linguistic knowledge graphs to guide attention mechanisms for the clickbait detection task. Extensive experimental results show that the proposed model outperforms existing state-of-the-art clickbait classifiers, even when training data is limited. The proposed model also performs better or comparably to powerful pre-trained models, namely, BERT, RoBERTa, and XLNet, while being much more lightweight. Furthermore, we conducted experiments to demonstrate that the use of human semantic knowledge can significantly enhance the performance of pre-trained models in the semi-supervised domain such as BERT, RoBERTa, and XLNet.

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“…However, privacy and security concerns exist since it is easy to steal deep learning models or change private training data [ 24 , 25 ]. Blockchain technology has gained widespread acceptance as a security solution as it ensures security, efficiency, and reduces fraud in CNNs [ 26 , 27 , 28 ]. Blockchain technology can prevent the manipulation of object labels by incorporating critical features such as security, authenticity, data privacy, and de-tracking [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things

Razaque,

Bektemyssova,

Yoo

et al. 2023

Sensors

Self Cite

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Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle’s nature to the “vehicle management system.” C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction.

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Clickbait Detection Using Deep Recurrent Neural Network

Cited by 11 publications

References 16 publications

Identification of clickbait news articles using SBERT and correlation matrix

Identification of clickbait news articles using SBERT and correlation matrix

An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection

Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things

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