A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

Imam, Niddal, Issac, Biju and Jacob, Seibu Mary (2019) A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift. International Journal of Computational Intelligence and Applications, 18 (02). p. 1950010. ISSN 1469-0268

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Manuscript_SSLA for Tackling Twitter Spam Drift.pdf - Accepted Version
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Official URL: https://doi.org/10.1142/S146902681950010X

Abstract

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

Item Type: Article
Uncontrolled Keywords: Semi-supervised learning; twitter spam; machine learning; spam drift.
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
Depositing User: Elena Carlaw
Date Deposited: 16 Apr 2019 08:40
Last Modified: 11 Oct 2019 10:04
URI: http://nrl.northumbria.ac.uk/id/eprint/38999

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