Intelligent Twitter Spam Detection: A Hybrid Approach

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 18)


Over the years there has been a large upheaval in the social networking arena. Twitter being one of the most widely-used social networks in the world has always been a key target for intruders. Privacy concerns, stealing of important information and leakage of key credentials to spammers has been on the rise. In this paper, we have developed an Intelligent Twitter Spam Detection System which gives the precise details about spam profiles by identifying and detecting twitter spam. The system is a Hybrid approach as opposed to single-tier, single-classifier approaches which takes into account some unique feature sets before analyzing the tweets and also checks the links with Google Safe Browsing API for added security. This in turn leads to better tweet classification and improved as well as intelligent twitter spam detection.


Twitter Spam Machine learning Google safe browsing Hybrid classifiers 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyMIT College of EngineeringPuneIndia
  2. 2.Department of Computer EngineeringMAEER’s MITPuneIndia
  3. 3.MIT School of Telecom & Management StudiesPuneIndia

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