Ban, Xinbo, Chen, Chao, Liu, Shigang, Wang, Yu, and Zhang, Jun (2019) Deep-learnt features for Twitter spam detection. In: Proceedings of the 2018 International Symposium on Security and Privacy in Social Networks and Big Data. pp. 208-212. From: SocialSec 2018: International Symposium on Security and Privacy in Social Networks and Big Data, 10-11 December 2018, Santa Clara, CA, USA.

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Abstract

Twitter spam has become one of the most critical problems in recent years. Despite the efforts of researchers and security companies, the growing number of spam is not stopping. Machine learning is a very popular technology in network security and is also used for spam detection. An important step of applying machine learning for Twitter spam detection is feature engineering. Existing works mainly use URL based features, meta-data based features and social relation based features to detect spam tweets. All of the abovementioned works require human effort to extract features. More recently, deep learning has shed its light on automated feature engineering in extracting features from text. In this paper, we propose a new feature engineering mechanism based on a deep neural network trained using Bi-LSTM. We name the extracted features "deep-learnt features". We compare ourfeature set with word2vec features and statistical features in the experimental evaluation. The results show that machine learning models trained using deep-learnt features can detect Twitter spam more accurately than models trained usingword2vec features and statistcal features.

Item ID:	64429
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-0816-2
Copyright Information:	(C) IEEE.
Date Deposited:	14 Oct 2020 02:10
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460499 Cybersecurity and privacy not elsewhere classified @ 100%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8903 Information Services > 890399 Information Services not elsewhere classified @ 100%
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