论文标题
在社交媒体上针对恶意攻击者的强大意见检测方法
A Robust Opinion Spam Detection Method Against Malicious Attackers in Social Media
论文作者
论文摘要
在线评论是行业所有者和买家的有效资源，但是机会主义者可能会试图通过发表名为“垃圾邮件意见”的伪造评论来破坏或推广其所需的产品。到目前为止，已经开发了许多模型来检测垃圾邮件意见，但没有人解决垃圾邮件攻击问题。这是一种智能垃圾邮件发送者可以以他可以继续生成垃圾邮件的方式欺骗系统的一种方式，而不必担心被系统检测和阻止。在本文中，讨论了垃圾邮件攻击。此外，提出了一种强大的基于图的垃圾邮件检测方法。该方法分别估计了考虑可能的欺骗情况的评论，审阅者和产品的诚实，信任和可靠性值。本文还通过某些案例研究提出了与其他基于图的方法相比，提出的方法的效率。
Online reviews are potent sources for industry owners and buyers, however opportunistic people may try to destruct or promote their desired product by publishing fake comments named spam opinion. So far, many models have been developed to detect spam opinions, but none have addressed the issue of spam attack. It is a way a smart spammer can deceive the system in a manner in which he can continue generating spams without the fear of being detected and blocked by the system. In this paper, the spam attacks are discussed. Moreover, a robust graph-based spam detection method is proposed. The method respectively estimates honesty, trust and reliability values of reviews, reviewers, and products considering possible deception scenarios. The paper also presents the efficiency of the proposed method as compared to other graph-based methods through some case studies.
