Social-spam profile detection based on content classification and user behavior

Web-based social system enables new community-based opportunities for participants to engage, share and interact. The rapid growth of Facebook has triggered a dramatic increase in spam volume and sophistication. Spammers post their status or comment in Page to send spam content to their friends or o...

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Bibliographic Details
Published in2016 Eighth International Conference on Knowledge and Systems Engineering (KSE) pp. 264 - 267
Main Authors Thi-Hong Vuong, Van-Hien Tran, Minh-Duc Nguyen, Cam-Van Thi Nguyen, Thanh-Huyen Pham, Mai-Vu Tran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Buildings
Data models
Entropy
Facebook
Feature extraction
Image edge detection
social networks
spam accounts detection
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Summary:Web-based social system enables new community-based opportunities for participants to engage, share and interact. The rapid growth of Facebook has triggered a dramatic increase in spam volume and sophistication. Spammers post their status or comment in Page to send spam content to their friends or other users in the network. In this paper, we consider the problem of detecting spam accounts on Facebook based on comment content and user social behavior. We will propose a hybrid approach using Maximum Entropy (Maxent) model for classifying user comments as either spam or non-spam. We carefully conducted an empirical evaluation for our model on a large collection of comments in Vietnamese Facebook Pages and achieved promising results with an average accuracy of more than 90%.
DOI:10.1109/KSE.2016.7758064