Author : Mr.Yash Shailesh Thigale and Prof.Deipali V. Gore

Social network sites involve billions of users around the world wide. User interactions with these social sites, like twitter have a tremendous and occasionally undesirable impact implications for daily life. The major social networking sites have become a target platform for spammers to disperse a large amount of irrelevant and harmful information. Twitter, it has become one of the most extravagant platforms of all time and, most popular microblogging services which is generally used to share unreasonable amount of spam. Fake users send unwanted tweets to users to promote services or websites that do not only affect legitimate users, but also interrupt resource consumption. Furthermore, the possibility of expanding invalid information to users through false identities has increased, resulting in malicious content. Recently, the detection of spammers and the identification of fake users and fake tweets on Twitter has become an important area of research in online social networks (OSN). In this Paper, proposed the techniques used to detect spammers on Twitter. In addition, a taxonomy of Twitter spam detection approaches is presented which classifies techniques based on their ability to detect false content, URL-based, spam on trending issues. Twelve to Nineteen different features, including six recently defined functions and two redefined functions, identified to learn two machine supervised learning classifiers, in a real time data set that distinguish users and spammers.

Keywords: Machine Learning, Parallel Computing, Spam Detection, Scalability, Twitter