Leverage knowledge graph and GCN for fine-grained-level clickbait detection

Zhou, Mengxi; Xu, Wei; Zhang, Wenping; Jiang, Qiqi

doi:10.1007/s11280-022-01032-3

Cited by 10 publications

(6 citation statements)

References 43 publications

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“…According to Eberhard et al (2019), the number of Chinese language users is second only to the number of English language users in the globe. On the other hand, there are few publicly available datasets for detecting clickbait, and even fewer in Chinese (Zhou et al, 2022). Based on the preceding context and motivation, this study has some research questions that should be solved.…”

Section: Dta 582mentioning

confidence: 99%

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Wang,

Maslim,

Liu

2023

DTA

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PurposeA clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.Design/methodology/approachThis study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.FindingsThis research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.Originality/valueThe originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

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Section: Dta 582mentioning

confidence: 99%

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Wang,

Maslim,

Liu

2023

DTA

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“…The overall architecture of DIFFPOOL is shown in Figure 2. The message transmission mode of Graph Convolution Network (GCN) [7] is used in the single-layer GNN module to learn useful Graph classification representation. Given a stock correlation network ( )…”

Section: Hierarchical Representation and Graph Classification Algorit...mentioning

confidence: 99%

Research on Stock Index Prediction Based on Stock Correlation Network and Deep Learning

2023

AJCIS

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Aiming at the problem of stock index prediction, constructing a time series stock correlation network based on the fundamentals and technology of stock index components, then using the depth map neural network to learn the hierarchical representation of stock correlation network, and obtaining the candidate prediction signal in an end-to-end way. The architecture composed of depth map neural network method and end-to-end strategy is called DIFFPOOL architecture. Taking the CSI 300 index as the research object, combining the DIFFPOOL architecture with softmax classifier, long-term and shortterm memory neural network (LSTM), linear regression, and logical regression, respectively, and uses the sliding time window method to obtain the corresponding prediction accuracy of stock index. The accuracy of the combined model under the optimal parameters fluctuates in the interval [0.56, 0.62]. Ultimately, the first mock exam is based on the mean absolute error (MAE) and the root mean square error (RMSE). The regression models of DIFFPOOL and regression models are compared with LSTM, recurrent neural network (RNN), and back propagation neural network (BP). Compared with the single model, the MAE and RMSE of the combined model are smaller, 0.0061 and 0.0081, respectively. Experiments show that by aggregating the node attribute information of the stock association network hierarchically, we can dynamically capture the impact of different industry sectors on stock index price fluctuations and further improve prediction accuracy.

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“…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]. Zhou et al proposed a clickbait detection model based on graph convolutional networks, and evaluated the model using a dataset in the Chinese language [44]. Recent deep learning models for clickbait detection [20], [21], [45] adapted and fine-tuned transformer models such as BERT [46], RoBERTa [47], XLNet [48], ELECTRA [49] and ALBERT [50].…”

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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Leverage knowledge graph and GCN for fine-grained-level clickbait detection

Cited by 10 publications

References 43 publications

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Research on Stock Index Prediction Based on Stock Correlation Network and Deep Learning

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

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