期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：21
页码：5881
出版社：Journal of Theoretical and Applied
摘要：Spam has been a major and global threat, Social networks have become our daily live and everyday tools, while different social networks have different target groups. With the rapid growth of social networks, people tend to misuse them for unethical and illegal conducts, fraud and phishing. Creation of a fake profile becomes such adversary effect which is difficult to identify without appropriate research. The current solutions that have been practically developed and theorized to solve this issue of spam detection issue and spam identification of fake profiles, primarily considered the characteristics and the social network ties of the user social profile. However, when it comes to social networks like Facebook, Twitter, SinaWeibo, Myspace, Tagged and LinkedIn such a behavioural observations are highly restrictive in publicly available profile data for the users by the privacy policies. The limited publicly available profile data of social networks makes it ineligible in applying the existing approaches and techniques in fake profile spam identification. Therefore, there is a need to conduct targeted research on identifying approaches for fake profile spam identification on selected and available data set of Facebook, Twitter and Sina weibo. In this research, we identify the minimal set of profile data that are necessary for identifying Fake profiles in Facebook, Twitter and Sina weibo and identifying the appropriate data mining approach and techniques for such task. We demonstrate that with limited profile data our approach can identify the fake profile with 84 % accuracy and 2.44 % false negative, which is comparable to the results obtained by other existing approaches based on the larger data set and more profile information.
