A comparative study of the class imbalance problem in Twitter spam detection. (17th September 2017)

• Record Type: Journal Article
• Authors: Li, Chaoliang; Liu, Shigang
• Other Names: Kutylowski Miroslaw guestEditor.; Wang Yu guestEditor.; Xu Shouhuai guestEditor.; Yang Laurence T. guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 5(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-17
• Subjects: classification -- class imbalance -- online social network -- Twitter spam detection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4281
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Recently, online social network (OSN) such as Twitter has become an important and popular source for real‐time information and news dissemination, and Twitter is inevitably a prime target of spammers. It has been showed that the security threats caused by Twitter spam can reach far beyond the social media platform itself. To mitigate the damage caused by Twitter spam, machine learning classification algorithms have been employed by researchers and communities to detect the Twitter spam. However, most of these studies have overlooked the class imbalance problem in Twitter spam detection. In this paper, we have studied the class imbalance problem in Twitter spam detection. Firstly, we have conducted a comparative study regarding some popular methods in handling the class imbalance problem in order to identify the most effective approach for addressing the class imbalance problem. Then, we have conducted another comparative study from Twitter spam detection based on several classic techniques. Experimental results demonstrate that a fuzy‐based ensemble learning can significantly improve the classification performance on imbalance ground truth Twitter data.