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A Machine Learning Approach towards Social Media to Improving the Performance

Jinu P Sainudeen1 , Sujitha M2 , Simy Mary Kurian3 , Neethu Maria John4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 956-960, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.956960

Online published on Jan 31, 2019

Copyright © Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John, “A Machine Learning Approach towards Social Media to Improving the Performance,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.956-960, 2019.

MLA Style Citation: Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John "A Machine Learning Approach towards Social Media to Improving the Performance." International Journal of Computer Sciences and Engineering 7.1 (2019): 956-960.

APA Style Citation: Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John, (2019). A Machine Learning Approach towards Social Media to Improving the Performance. International Journal of Computer Sciences and Engineering, 7(1), 956-960.

BibTex Style Citation:
@article{Sainudeen_2019,
author = {Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John},
title = {A Machine Learning Approach towards Social Media to Improving the Performance},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {956-960},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5505},
doi = {https://doi.org/10.26438/ijcse/v7i1.956960}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.956960}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5505
TI - A Machine Learning Approach towards Social Media to Improving the Performance
T2 - International Journal of Computer Sciences and Engineering
AU - Jinu P Sainudeen, Sujitha M, Simy Mary Kurian, Neethu Maria John
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 956-960
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

The predominance of web-based entertainment is growing step by step y. Individuals of all age bunch are horribly intrigued by long range informal communication. Web-based entertainment associates individuals from various areas of the planet. In any case, online entertainment might have a few aftereffects, for example, digital tormenting, which might adversely affect the existence of individuals. Research shows that youngsters and teens are the fundamental survivors of this digital assault. Through the virtual entertainment, individuals share their considerations and feelings with their companions. There are enormous quantities of misrepresentation accounts in virtual entertainment. Digital tormenting is the point at which somebody, disturb others via web-based entertainment locales. Certain individuals use it for digital assault by offering negative remarks on others post. One method for handling this issue is to identify those harassing messages and scramble it. AI procedures make programmed identification of digital tormenting messages. Weka is a power full AI instrument which can be utilized for this reason. A mix of grouping and lexical algorithms can recognize regardless of whether a message is harassing.

Key-Words / Index Term

Machine learning, Weka, Classification algorithms, Lexical analysis

References

[1] A. M. Kaplan and M. Haenlein, “Users of the world, unite! The challenges and opportunities of social media,” Business horizons, vol. 53, Issue 1, pp. 59–68, 2010.
[2] R. M. Kowalski, G. W. Giumetti, A. N. Schroeder, and M.R. Lattanner, “Bullying in the digital age: A critical review and meta-analysis of cyber bullying research among youth.”Vol.140,Issue 4.pp.1073-1137, 2014.
[3] M. Ybarra, “Trends in technology-based sexual and non- sexual aggression over time and linkages to nontechnology aggression, “National summit on interpersonal violence and abuse across the lifespan: Forging a Shared Agenda, 2010.
[4] B. K. Biggs, J. M. Nelson, and M. L. Sampilo, “Peer relations inthe anxiety–depression link: Test of a mediation model,” Anxiety, Stress, & Coping, Vol. 23, Issue. 4, pp. 431– 447, 2010.
[5] S. R. Jimerson, S. M. Swearer, and D. L. Espelage,” Handbook of bullying in schools”: An international perspective. Routledge/Taylor & Francis Group, 2010.
[6] G. Gini and T. Pozzoli, “Association between bullying and psychosomaticproblems: A meta-analysis,” Pediatrics, Vol. 123, Issue. 3, pp. 1059–1065, 2009.
[7] ‘Barbara Trolley, Ph.D. CRCConnie Hanel, M.S.E.d & Linda Shields “Demystifying and Deescalating Cyber Bullying”, 2006
[8] Vinita Nahar, Xue Li, Chaoyi Pang “An Effective Approach for Cyberbullying Detection,”Vol 40 .,Issue.1,2016
[9] Qianjia Huang , Vivek K. Singh “Cyber Bullying Detection using Social and Textual Analysis” https://doi.org/10.1145/2661126.2661133,pp.3-6,2014.
[10] Vinita Nahar, Xue Li, Chaoyi Pang “,An Effective Approach for Cyberbullying Detection”,chool of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, Queensland 4072, Australia.
[11] Karthik Dinakar, Roi Reichart Henry, Lieberman. MIT Media Lab,”Modeling the Detection of Textual Cyberbullying. Computer Science & Artificial Intelligence” Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 the USA.
[12] Winnipeg MB, Vivek K. Singh ”Cyber Bullying Detection Using Social and Textual Analysis” Qianjia Huang Department of Applied Computer Science The University of The Media Lab Massachusetts Institute of Technology Cambridge.