IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v9y2023i3idhcseit2390337.html

Fake Profile Identification in Social Network Using Machine Learning and NLP

Author

Listed:
  • A Hima Bindu
  • Sheelam Sriya Reddy
  • Gurrala Sai Sri

Abstract

At present social network sites are part of the life for most of the people. Every day several people are creating their profiles on the social network platforms and they are interacting with others independent of the user’s location and time. The social network sites not only providing advantages to the users and also provide security issues to the users as well their information. To analyze, who are encouraging threats in social network we need to classify the social networks profiles of the users. From the classification, we can get the genuine profiles and fake profiles on the social networks. Traditionally, we have different classification methods for detecting the fake profiles on the social networks. But, we need to improve the accuracy rate of the fake profile detection in the social networks. In this paper we are proposing Machine learning and Natural language Processing (NLP) techniques to improve the accuracy rate of the fake profiles detection. We can use the Support Vector Machine (SVM) and Naïve Bayes algorithm.

Suggested Citation

  • A Hima Bindu & Sheelam Sriya Reddy & Gurrala Sai Sri, 2023. "Fake Profile Identification in Social Network Using Machine Learning and NLP," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 9(3), pages 167-172, June.
    • Handle: RePEc:jbh:ijsrcs:v9:y2023:i3:id:hcseit2390337
    Note: Article URL: https://ijsrcseit.com/CSEIT2390337
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT2390337
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT2390337.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v9:y2023:i3:id:hcseit2390337. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.