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 12 publications

(1 citation statement)

References 26 publications

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“…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

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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.

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Section: Deep Learning (Dl) Approaches For Spam Classificationmentioning

confidence: 99%