Detection of Twitter Spam's using Machine Learning Algorithm
|International Journal of Computer Science and Engineering|
|© 2019 by SSRG - IJCSE Journal|
|Volume 6 Issue 3|
|Year of Publication : 2019|
|Authors : K. Jino Abisha, J.Roshan Nilofer, A.Silviya, Dr. S. Raja Ratna|
K. Jino Abisha, J.Roshan Nilofer, A.Silviya, Dr. S. Raja Ratna, "Detection of Twitter Spam's using Machine Learning Algorithm" SSRG International Journal of Computer Science and Engineering 6.3 (2019): 10-13.
K. Jino Abisha, J.Roshan Nilofer, A.Silviya, Dr. S. Raja Ratna,(2019). Detection of Twitter Spam's using Machine Learning Algorithm. SSRG International Journal of Computer Science and Engineering 6(3), 10-13.
With the increased popularity of online social networks, spammers find these platforms easily accessible to trap users in malicious activities by posting spam messages. In this work, Twitter platform is taken and spam tweets detection is performed. To stop spammers, semi supervised learning is used to detect spam tweets in twitter. Thus, industries and researchers have applied different approaches to make spam free social network platform. Some of them are only based on user-based features while others are based on tweet based features only. To solve this issue, a framework has been proposed which takes the user and tweet based features along with the tweet text feature to classify the tweets. The benefit of using tweet text feature is that the spam tweets can be identified even if the spammer creates a new account which was not possible only with the user and tweet based features. The work has been evaluated with three different machine learning algorithms namely - Support Vector Machine, Neural Network, Random Forest. With Naive Bayes classifier, about 80% of accuracy is obtained.
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Twitter, spam, supervised learning, support vector.