Spam Detection in Online Social Network with K-Means Clustering and SVM Machine Learning Approach
Rucha Narkhede, Rahul Gaikwad · International Journal of Innovations in Engineering and Science · 2021
Online social networks (OSNs) are becoming extremely popular among Internet users as they spend significant amount of time on popular social networking sites such as Facebook, Twitter and Google.These sites are turning out to be fundamentally pervasive and are developing a communication channel for billions of users.The dependence on these platforms for seeking opinions, news, and updates etc. is increasing.While it is true that OSNs have become a new medium for dissemination of information, at the same time, they are also fast becoming a playground for the spread of misinformation, propaganda, fake news, rumors and unsolicited messages.The huge amount of information available on these sites attracts the interest of cyber criminals who misuse these sites to exploit vulnerabilities for the illicit benefits like advertising some product or to attract victims to click on malicious links or infecting users system.Spam detection is one of the major problems now a day in social networking sites.Most previous techniques use different set of features to classify spam and non-spam users.In the paper, the machine learning algorithms such as K-Means Clustering, Support Vector Machine, and Emotional base spam word analyzer are applied and the spam words & emotions are detected from user's post.