Semi-Supervised Recursive Autoencoders for Social Review Spam Detection

被引：0

作者：

Wang, Baohua ^{[1
]}

Huang, Junlian ^{[1
]}

Zheng, Haihong ^{[1
]}

Wu, Hui ^{[2
]}

机构：

[1] Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China

[2] Capgemini China, CST Testing, Shenzhen 518000, Peoples R China

来源：

PROCEEDINGS OF 2016 12TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2016年

基金：

中国国家自然科学基金;

关键词：

Semi-supervised; Autoencoders; Review; Spam Detection;

D O I：

10.1109/CIS.2016.34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As spam hampers the productivity and performance of social media and causes erosion in the user base and thus associated financial loss, a semi-supervised recursive autoencoders model is applied to social review spams detection problem in this paper. The model is based on semi supervised recursive autoencoders, which learns vector representations of phrases and full sentences as well as their hierarchical structure from the text. This model exploits hierarchical structure and uses compositional semantics to understand meanings, without requiring any language-specific lexica, parsers or knowledge base. Experiments conducted on real dataset show that the approach can effectively detect the social review spams.

引用

页码：116 / 119

页数：4