Spammer detection and fake user identification using Machine Learning
Author(s):
Dr. M. Chinna Rao, M.Eswara Sai krishna, M.Suvarna Babu, A.Dheeraj Babu, L laban
Keywords:
Arguments, Broader Level, Outspread ,Taxonomy
Abstract
Online social Network has rapidly become an online source for acquiring real-time his/her information about users. Twitter is an Online Social Network (OSN) where users can share anything and everything, such as news, opinions, and even their moods. Several arguments can be held over different topics, such as politics, Particular affairs, and important events. When a user tweets something, it is instantly conveyed to her followers, allowing them to outspread the received information at a much broader level. The project proposes the detection of spammers and fake user identification on Twitter using various machine learning algorithms like Random forest, Naive Bayes and extreme machine learning algorithm. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: Fake content,Spam based on URL,Spam in trending topics,Fake users.The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform
Article Details
Unique Paper ID: 155864

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 113 - 120
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