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Reseach Article

Spam Filtering Methods and machine Learning Algorithm - A Survey

by Abha Tewari, Smita Jangale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 6
Year of Publication: 2016
Authors: Abha Tewari, Smita Jangale
10.5120/ijca2016912153

Abha Tewari, Smita Jangale . Spam Filtering Methods and machine Learning Algorithm - A Survey. International Journal of Computer Applications. 154, 6 ( Nov 2016), 8-12. DOI=10.5120/ijca2016912153

@article{ 10.5120/ijca2016912153,
author = { Abha Tewari, Smita Jangale },
title = { Spam Filtering Methods and machine Learning Algorithm - A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 6 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number6/26494-2016912153/ },
doi = { 10.5120/ijca2016912153 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:30.190352+05:30
%A Abha Tewari
%A Smita Jangale
%T Spam Filtering Methods and machine Learning Algorithm - A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 6
%P 8-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social networking websites are used by millions of people around the world. People express their views, opinions and share current topics. Millions of data generated every day. It’s a good platform to connect with the people. Now a day’s spammers used this platform to advertise spam content on the social networking websites. The proposed system used to classify tweets into different groups as spam and non spam tweets .The system will use 120 character tweets for analysis purpose. Various active and verified twitter accounts would be chosen to extract the tweets. Each tweet is to be classified into 2 category-spam and non-spam. These classified tweets then are used to train the various machine learning techniques. Words of each tweet considered as features and a feature vector was created using bag-of-words approach in order to create the instances. The data will be trained using SVM (Support Vector Machine).

References
  1. “Dynamic Feature Selection for Spam Filtering Using Support Vector Machine” by Md. Rafiqul Islam, Wanlei Zhou and Morshed U. Choudhury
  2. http://www.techsoup.org/learningcenter/internet/page6028.cfm
  3. http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/ “A Tour of Machine Learning Algorithms by Jason Brownlee on November 25, 2013 in Machine Learning Algorithms
  4. https://www.quora.com/What-are-some-interesting-possible-applications-of-machine-learning
  5. “A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection” Chao Chen, Jun Zhang, Member, IEEE, Yi Xie, Yang Senior Member, IEEE,@2015
  6. https://blog.bufferapp.com/the-5-types-of-tweets-to-keep-your-buffer-full-and-your-followers-engaged
  7. “Spam Filtering Techniques and MapReduce with SVM: A Study” by Amol G. Kakade, Prashant K. Kharat, Anil Kumar Gupta,Tarun Batra ©2014 IEEE
Index Terms

Computer Science
Information Sciences

Keywords

Spam tweets svm kernel functions SVMLIB