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Message Filtering on Social Media Content

Published on May 2015 by Jinju Joby P., Jyothi Korra
journal_cover_thumbnail

An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
Foundation of Computer Science USA
ICCTAC2015 - Number 1
May 2015
Authors: Jinju Joby P., Jyothi Korra
81bc1255-b6ce-423b-aa02-52934c104488

Jinju Joby P., Jyothi Korra . Message Filtering on Social Media Content. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 1 (May 2015), 1-4.

@article{
author = { Jinju Joby P., Jyothi Korra },
title = { Message Filtering on Social Media Content },
journal = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
issue_date = { May 2015 },
volume = { ICCTAC2015 },
number = { 1 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/icctac2015/number1/20916-2002/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%A Jinju Joby P.
%A Jyothi Korra
%T Message Filtering on Social Media Content
%J An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%@ 0975-8887
%V ICCTAC2015
%N 1
%P 1-4
%D 2015
%I International Journal of Computer Applications
Abstract

The social networking era has left us with little privacy. The details of the social network users are published on Social Networking sites. Vulnerability has reached new heights due to the overpowering effects of social networking. The sites like Facebook, Twitter are having a huge set of users who publish their files, comments, messages in other users' walls. These messages and comments could be of any nature. Even friends could post a comment that would harm a persons' integrity. Thus there has to be a system which will monitor the messages and comments that are posted on the walls. If the messages are found to be neutral (does not have any harmful content), then it can be published. If the messages are found to have non-neutral content in them, then these messages would be blocked by the social network manager. The messages that are non-neutral would be of sexual, offensive, hatred, pun intended nature. Thus the social network manager can classify content as neutral and non-neutral and notify the user if there seems to be messages of non-neutral behavior.

References
  1. Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati, Moreno Carullo, "A System to Filter Unwanted Messages from OSN User Walls," IEEE Transactions On Knowledge And Data Engineering Volume 25, 2013.
  2. Ms S. Vijayarani, Ms M. Muthulakshmi, "Comparative Analysis of Bayes and Lazy Classification Algorithms," International Journal of Advanced Research in Computer and Communication Engineering, Volume 2, Issue 8, August 2013.
  3. Prof. G. N. Purohit, Dr. Priti Singh, Mrs. Praveen Dangi, "Content Filtering On Social Networking Sites," International Journal of Advanced Trends in Computer Science and Engineering, Volume 3, January 2014.
  4. M. Chau and H. Chen, "A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis," Decision Support Systems, Volume 44, Pp. 482-494, 2008.
  5. N. J. Belkin, W. B. Croft, "Information Filtering and Information Retrieval: Two Sides of the Same Coin?" Comm. ACM, Volume 35, Pp. 29-38, 1992.
  6. Michael J. Pazzani, "A Framework for Collaborative, Content-Based and Demographic Filtering", Artificial Intelligence Review 13, Pp. 393–408, 1999.
  7. Boykin, Roychowdhury, "Leveraging social networks to fight spam", IEEE Computer Magazine 38, Pp. 61–67, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Neutral Messages Non Neutral Messages Social Networking