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A Multi-classifier Framework for Detecting Spam and Fake Spam Messages in Twitter

机译：用于在Twitter中检测垃圾邮件和虚假垃圾邮件的多分类器框架

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摘要

Social media plays vital role among the user communities for social gathering, entertainment, communication, sharing knowledge so on. Twitter is one such network to connect millions of users to share information. Nowadays, there are humpteen numbers of users using social media for social engagements. Due to the fact that wide publicity of individuals and products get viral in social media, everyone wish to use social media as a platform to promote their product. Furthermore, large number of people relies on social media contents to take decisions. Twitter is one of the social media platforms to post the media contents by the user. Spammers are illegal users intrude the twitter account and send the duplicate messages to promote advertisement, phishing, scam and personal blogs etc. In this paper, a novel spam detection mechanism is introduced to detect the suspicious users on twitter. The system has been designed such a way that it initially set with semi-supervised at the tweet level and update into supervised level for learning the input tweets to detect the spammers. The proposed system will also identify the type of spammers and will also remove duplicate tweets. We have applied with multi-classifier algorithms like naïve Bayesian, K-Nearest neighbor and Random forest into twitter data set and the performance is compared. The experimental result shows very promising results.
机译：社交媒体在用户社区之间的社交，娱乐，交流，知识共享等方面起着至关重要的作用。 Twitter是连接数百万用户共享信息的网络之一。如今，有不计其数的用户使用社交媒体进行社交活动。由于个人和产品的广泛宣传在社交媒体中广为传播，每个人都希望将社交媒体用作推广其产品的平台。此外，大量人依赖于社交媒体内容来做出决定。 Twitter是社交媒体平台之一，由用户发布媒体内容。垃圾邮件发送者是非法用户，它们侵入Twitter帐户并发送重复消息，以宣传广告，网络钓鱼，欺诈和个人博客等。本文介绍了一种新颖的垃圾邮件检测机制，用于检测Twitter上的可疑用户。该系统的设计方式使其最初在推文级别设置为半监督，然后更新为监督级别，以学习输入推文以检测垃圾邮件发送者。拟议的系统还将识别垃圾邮件发送者的类型，并将删除重复的推文。我们已将诸如朴素贝叶斯，K最近邻和随机森林等多分类器算法应用到twitter数据集中，并对性能进行了比较。实验结果显示了非常有希望的结果。

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