论文标题
Twitter垃圾邮件检测：系统评价
Twitter Spam Detection: A Systematic Review
论文作者
论文摘要
如今，随着全球互联网访问和移动设备的兴起，越来越多的人正在使用社交网络进行协作和接收实时信息。 Twitter是正在成为沟通和新闻传播的关键来源的微博，它引起了垃圾邮件发送者的注意，以分散用户的注意力。到目前为止，研究人员已经引入了各种防御技术，以在Twitter上检测垃圾邮件和打击垃圾邮件发送者。为了克服这个问题，近年来，研究人员提供了许多新型技术，这些技术大大提高了垃圾邮件检测性能。因此，它提出了对Twitter上垃圾邮件检测的不同方法进行系统评价的动力。这篇评论重点是系统地比较Twitter垃圾邮件检测的现有研究技术。文献综述分析表明，大多数现有方法都依赖于基于机器学习的算法。在这些机器学习算法中，主要差异与各种特征选择方法有关。因此，我们提出了一种基于不同特征选择方法和分析的分类法，即内容分析，用户分析，推文分析，网络分析和混合分析。然后，我们对当前方法进行了数值分析和比较研究，提出了公开的挑战，可以帮助研究人员在此主题中开发解决方案。
Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging that is becoming a critical source of communication and news propagation, has grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammer activities on Twitter. To overcome this problem, in recent years, many novel techniques have been offered by researchers, which have greatly enhanced the spam detection performance. Therefore, it raises a motivation to conduct a systematic review about different approaches of spam detection on Twitter. This review focuses on comparing the existing research techniques on Twitter spam detection systematically. Literature review analysis reveals that most of the existing methods rely on Machine Learning-based algorithms. Among these Machine Learning algorithms, the major differences are related to various feature selection methods. Hence, we propose a taxonomy based on different feature selection methods and analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Then, we present numerical analyses and comparative studies on current approaches, coming up with open challenges that help researchers develop solutions in this topic.
