Investigating the deceptive information in Twitter spam

被引：32

作者：

Chen, Chao ^{[1
]}

Wen, Sheng ^{[1
]}

Zhang, Jun ^{[1
]}

Xiang, Yang ^{[1
]}

Oliver, Jonathan ^{[2
]}

Alelaiwi, Abdulhameed ^{[3
]}

Hassan, Mohammad Mehedi ^{[3
]}

机构：

[1] Deakin Univ, Sch Informat Technol, 221 BurwoodHwy, Burwood, Vic 3125, Australia

[2] Trend Micro, 606 St Kilda Rd, Melbourne, Vic 3004, Australia

[3] King Saud Univ, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2017年 / 72卷

基金：

澳大利亚研究理事会;

关键词：

Online Social Network; Big data; Twitter spam analysis;

D O I：

10.1016/j.future.2016.05.036

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Online Social Networks (OSNs) such as Facebook and Twitter have become popular communication and information sharing tools for hundreds of millions of individuals in recent years. OSNs not only make people's life more connected, but also attract the interest of spammers. Twitter spam generally contains deceptive information, such as "free voucher" and "weight loss advertisement" to attract the interest of victims. A comprehensive analysis on the deceptive information will be of great benefit to the detection of Twitter spam. This paper presents a study of deceptive information in Twitter spam. The analysis is based on a collection of over 550 million tweets with around 6% spam. We find that various deceptive content of spam performs differently in luring victims to malicious sites. We also find the regional response rate to various Twitter spam outbreaks varies greatly. These two factors can contribute to improve the performance of spam detection techniques. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：319 / 326

页数：8

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