Detection of Malicious URLs in Twitter

Abstract

With spam filtering techniques have been improved in social websites like G mail., spammers find their place in other famous social platforms like Twitter, Facebook. Therefore, an effective spam filtering technology is essential for platforms like Twitter, Facebook, etc. We have developed a web application that will be able to find out whether a particular tweet from Twitter is malicious or non- malicious based on the Url that the tweet possesses by considering both text-based and Url-based features. We have employed machine learning techniques to classify the tweet content after preprocessing the data that we have fetched from Twitter with the help of tokens that we obtain after creating the Twitter developer account. We are classifying a tweet based on five different features, these features can be most commonly found in malicious tweets as per our research. The results that are obtained from our experiment show that our approach could efficiently identify malicioustweets.