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Spam Detection on Social Media Using Semantic Convolutional Neural Network

Spam Detection on Social Media Using Semantic Convolutional Neural Network

Gauri Jain, Manisha Sharma, Basant Agarwal
Copyright: © 2020 |Pages: 16
ISBN13: 9781799804147|ISBN10: 1799804143|EISBN13: 9781799804154
DOI: 10.4018/978-1-7998-0414-7.ch039
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MLA

Jain, Gauri, et al. "Spam Detection on Social Media Using Semantic Convolutional Neural Network." Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 704-719. https://doi.org/10.4018/978-1-7998-0414-7.ch039

APA

Jain, G., Sharma, M., & Agarwal, B. (2020). Spam Detection on Social Media Using Semantic Convolutional Neural Network. In I. Management Association (Ed.), Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 704-719). IGI Global. https://doi.org/10.4018/978-1-7998-0414-7.ch039

Chicago

Jain, Gauri, Manisha Sharma, and Basant Agarwal. "Spam Detection on Social Media Using Semantic Convolutional Neural Network." In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 704-719. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0414-7.ch039

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Abstract

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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