CYBERBULLYING REVELATION IN TWITTER DATA USING NAÏVE BAYES CLASSIFIER ALGORITHM

Binsu C Kovoor, Vandana Nandakumar, Sreeja M. U.

Abstract


Cyberbullying can be visualized as a potential issue affecting children and all categories of people. One demanding concern is effective representation for learning of content messages. The proposed system deals with cyberbullying revelation in email application using Naive Bayes Classifier Algorithm. The Classification Algorithm is a baseline method for content classification; the method of analyzing documents as relating to one classification or the other with word prevalence as features. The technique deals with the identification and filtering of spam words. The denoised messages are classified with the help of Naive Bayes Classifier Algorithm. The messages are processed under feature set extraction method. The feature probabilities are found out using Naive Bayes Classifier Algorithm .The efficiency factor is compared among the two algorithms, Naive Bayes Classifier Algorithm and Support Vector Machine and a graph is plotted. Comparison on the basis of precision factor is also done with the fact that the probabilities for each feature set are calculated independently from the twitter dataset and can evaluate the performance by predicting the output variable.

Keywords


twitter; spam;Naïve Bayes classifier; support vector machine

Full Text:

PDF

References


Weider D. Yu, Maithili Gole, Nishanth Prabhuswamy, Sowmya Prakash, Vidya Gowdru Shankaramurthy, “An Approach To Design and Analyze the Framework for Preventing Cyberbullying”, IEEE International Conference on Services Computing, 2016, pp.864-867, doi: 10.1109/SCC.2016.125 .

Samaneh Nadali, Masrah Azrifah Azmi Murad, “A Review of Cyberbullying Detection :An Overview”, International Cmiference on Intelligent Systems Design and Applications (ISDA), 2013, pp.325-330, doi:10.1109/ISDA.2013.6920758 .

Amrita Mangoankar, Allenoush Hayrapetian, ” Collaborative Detection of Cyberbullying Behaviour In Twitter Data”, 2015, IEEE Conference, pp.612-616, doi:10.1109/EIT.2015.7293405 .

Silvana Tambosi, Gustavo Borges, Vanessa Mondin, Maria José Domingues, ” Cyberbullying: Concerns of Teachers and School Involvement”, International Conference on Complex, Intelligent, and Software Intensive Systems Cyberbullying, 2015, pp.416-420, doi: 10.1109/CISIS.2015.92

Kelly Reynolds, April Kontostathis, Lynne Edwards, ” Using Machine Learning to Detect Cyberbullying”, International Conference on Machine Learning and Applications, 2011, pp.242-244, doi:10.1109/ICMLA.2011.152 .

Ana Kovacevic, ”Cyberbullying Detection Using Web content Mining”, Telecommunications forum TELFOR, 2014, pp. 25-27, doi:10.1109/TELFOR.2014.7034560 .

Tibor Bosse, Sven Stam ,”A Normative Agent System To Prevent Cyberbullying”, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 425-430, doi: 10.1109/WI-IAT.2011.24.

Homa Hosseinmardi Shaosong Li, Zhili Yang and Qin Lv, Rahat Ibn Rafiq Richard Han and Shivakant Mishra,” A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying”, IEEE International Conference on Big Data and Cloud Computing, 2014, pp. 355-362, doi: 10.1109/BDCloud.2014.87.

Nektaria Potha, Manolis Maragoudakis,” Cyberbullying Detection using Time Series Modeling”, IEEE International Conference on Data Mining Workshop, 2014, pp.373-382, doi:10.1109/ICDMW.2014.170 .

Marlies Rybnicek, Rainer Poisel and Simon Tjoa, ”Facebook Watchdog:A Research Agenda For Detecting Online Grooming And Bullying Activities”, IEEE International Conference on Systems, Man, and Cybernetics, 2013, pp.2854-2859, doi: 10.1109/SMC.2013.487 .

Rekha Sugandhi, Anurag Pande, “Method for Detection of Cyberbullying:A Survey”, International Conference on Intelligent Systems Design and Applications (ISDA), 2015, pp.173-177, doi: 10.1109/ISDA.2015.7489220 .

Zinnar Ghasem, Ingo Frommhoz, ”A Hybrid Aprroach to Combat Email Based Cyberstalking”, FGCT International Conference on Future Generation Communication Technologies, 2015, pp.106-111, doi:10.1109/FGCT.2015.7300257




DOI: https://doi.org/10.26483/ijarcs.v9i1.5396

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 International Journal of Advanced Research in Computer Science