Paper Title:Malicious URLs and Spam Detection in Social Network using Machine Learning Approach


Twitter is prostrate to malicious tweets having URLs for spam circulation. Regular Twitter spam discovery techniques exploit account highlights, for example, the proportion of tweets containing URLs and the date of making a record, or connection includes in the Twitter diagram. These location strategies are incapable against highlight manufactures or devour much time and resources. In this paper we have proposed a machine learning system to discover Malicious URLs and spam and to recognize whether a given tweet is spamming of not in a Social Network, for example, Twitter. By gathering dataset and preparing the classifier we ordered the info tweet. The Naive Bayes calculation, a regulated learning model with related learning calculations which are utilized to break down information utilized for grouping and relapse examination. After arrangement the affect ability of each tweet is ascertained. After trial comes about it is discovered that the prepared classifier is appeared to be exact and has low false positives and negatives.
Keywords:Classification, Stemming, Naïve Bayes, Suspicious URL.