A Content-Based Chinese Spam Detection Method Using a Capsule Network With Long-Short Attention

Tong, Xin; Wang, Jingya; Zhang, Changlin; Wang, Runzheng; Ge, Zhilin; Liu, Wenmao; Zhao, Zijian

doi:10.1109/jsen.2021.3092728

Cited by 15 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Barushka and Hajek [30] presented an integrated distribution-based balancing approach and regularized deep neural networks for spam filtering. Tong et al [31] proposed a capsule network model combining long-short attention for detecting Chinese spam, which used a multi-channel structure based on the long-short attention mechanism. According to the experimental results, the model outperformed the current mainstream methods, such as TextCNN, LSTM, and even BERT, in characterization and detection.…”

Section: Detection Methods Based On Deep Learningmentioning

confidence: 99%

Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

Yao

Wang

et al. 2022

Electronics

View full text Add to dashboard Cite

The proliferation of spam in China has a negative impact on internet users’ experiences online. Existing methods for detecting spam are primarily based on machine learning. However, it has been discovered that these methods are susceptible to adversarial textual spam that has frequently been imperceptibly modified by spammers. Spammers continually modify their strategies to circumvent spam detection systems. Text with Chinese homophonic substitution may be easily understood by users according to its context. Currently, spammers widely use homophonic substitution to break down spam identification systems on the internet. To address these issues, we propose a Bidirectional Gated Recurrent Unit (BiGRU)–Text Convolutional Neural Network (TextCNN) hybrid model with joint embedding for detecting Chinese spam. Our model effectively uses phonetic information and combines the advantages of parameter sharing from TextCNN with long-term memory from BiGRU. The experimental results on real-world datasets show that our model resists homophone noise to some extent and outperforms mainstream deep learning models. We also demonstrate the generality of joint textual and phonetic embedding, which is applicable to other deep learning networks in Chinese spam detection tasks.

show abstract

Section: Detection Methods Based On Deep Learningmentioning

confidence: 99%

Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

Yao

Wang

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…On spam text information obtained from the UCI machine learning repository, they achieved a 99% accuracy. Tong et al (2021) used a deep learning model based on LSTM and BERT to overcome issues such as unfair representation, inadequate detection effect, and poor practicality in Chinese spam detection. They created this model to capture complex text features using a long-short attention mechanism.…”

Section: Deep Learning (Dl) Approaches For Spam Classificationmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

show abstract

A Comprehensive Survey of Datasets Used for Spam and Genuineness Views Detection in Twitter

Torney

Walse²,

Thakare

2022

Computational Intelligence and Data Analytics

View full text Add to dashboard Cite

A Content-Based Chinese Spam Detection Method Using a Capsule Network With Long-Short Attention

Cited by 15 publications

References 26 publications

Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

A systematic literature review on spam content detection and classification

A Comprehensive Survey of Datasets Used for Spam and Genuineness Views Detection in Twitter

Contact Info

Product

Resources

About