• DocumentCode
    133942
  • Title

    An integrated approach to spam classification on Twitter using URL analysis, natural language processing and machine learning techniques

  • Author

    Kandasamy, Kamalanathan ; Koroth, Preethi

  • Author_Institution
    Amrita Center for Cyber Security, Amrita Vishwa Vidyapeetham, Kollam, India
  • fYear
    2014
  • fDate
    1-2 March 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    In the present day world, people are so much habituated to Social Networks. Because of this, it is very easy to spread spam contents through them. One can access the details of any person very easily through these sites. No one is safe inside the social media. In this paper we are proposing an application which uses an integrated approach to the spam classification in Twitter. The integrated approach comprises the use of URL analysis, natural language processing and supervised machine learning techniques. In short, this is a three step process.
  • Keywords
    classification; learning (artificial intelligence); natural language processing; social networking (online); unsolicited e-mail; Twitter; URL analysis; natural language processing; social media; social networks; spam classification; spam contents; supervised machine learning techniques; Accuracy; Machine learning algorithms; Natural language processing; Training; Twitter; Unsolicited electronic mail; URLs; machine learning; natural language processing; tweets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students' Conference on
  • Conference_Location
    Bhopal
  • Print_ISBN
    978-1-4799-2525-4
  • Type

    conf

  • DOI
    10.1109/SCEECS.2014.6804508
  • Filename
    6804508