CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Diametrical User Control to Filter the Unwanted Messages from Online Social Networks

by M. Swapna, P.Rajarajeswari, D.Vasumathi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 17
Year of Publication: 2014
Authors: M. Swapna, P.Rajarajeswari, D.Vasumathi
10.5120/18301-9153

M. Swapna, P.Rajarajeswari, D.Vasumathi . Diametrical User Control to Filter the Unwanted Messages from Online Social Networks. International Journal of Computer Applications. 103, 17 ( October 2014), 31-32. DOI=10.5120/18301-9153

@article{ 10.5120/18301-9153,
author = { M. Swapna, P.Rajarajeswari, D.Vasumathi },
title = { Diametrical User Control to Filter the Unwanted Messages from Online Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 17 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 31-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number17/18301-9153/ },
doi = { 10.5120/18301-9153 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:50.789585+05:30
%A M. Swapna
%A P.Rajarajeswari
%A D.Vasumathi
%T Diametrical User Control to Filter the Unwanted Messages from Online Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 17
%P 31-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online Social Networks (OSNs) are the most impulsive standard to provide communication, sharing and disseminate a significant amount of individual life data. Content can be changed based on daily needs in several communication fields (audio, video, image, and text). One of the important aspects of OSN is to give the user capacity to automatically manage the messages posted and also to filter the unnecessary messages. The main aim of my work is to evaluate an automated system called Filter walls and also using Machine learning text categorization technique. These techniques automatically label the messages in support of substance-based filtering. This application focuses on Face book where unknown persons post unwanted comments for the uploaded photos. So, our system provides help and security to our profiles by directly controlling what kind of messages should be posted on our own walls.

References
  1. M. Chau and H. Chen, “A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482-494, 2008.
  2. R.J. Mooney and L. Roy, “Content-Based Book Recommending Using Learning for Text Categorization,” Proc. Fifth ACM Conf. Digital Libraries, pp. 195-204, 2000.
  3. F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
  4. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-Based Filtering in On-Line Social Networks,” Proc. ECML/PKDD Workshop Privacy and Security Issues in Data Mining and Machine Learning (PSDML ’10), 2010.
  5. N.J. Belkin and W.B. Croft, “Information Filtering and Information Retrieval: Two Sides of the Same Coin?” Comm. ACM, vol. 35, no. 12, pp. 29-38, 1992.
  6. P.J. Denning, “Electronic Junk,” Comm. ACM, vol. 25, no. 3, pp. 163-165, 1982.
  7. P.W. Foltz and S.T. Dumais, “Personalized Information Delivery: An Analysis of Information Filtering Methods,” Comm. ACM, vol. 35, no. 12, pp. 51-60, 1992. P.S. Jacobs and L.F. Rau, “Scisor: Extracting Information from On- Line News,” Comm. ACM, vol. 33, no. 11, pp. 88 97, 1990.
  8. S. Pollock, “A Rule-Based Message Filtering System,” ACM Trans. Office Information Systems, vol. 6, no. 3, pp. 232-254, 1988.
  9. P.E. Baclace, “Competitive Agents for Information Filtering,” Comm. ACM, vol. 35, no. 12, p. 50, 1992.
  10. M.J. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, no. 3, pp. 313-331, 1997.
Index Terms

Computer Science
Information Sciences

Keywords

OSNs Machine Learning (ML) Text Categorization RBFN

Powered by PhDFocusTM