Artificial Intelligent Systems and Machine Learning

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Cyberbully Detection from Twitter Using Classifiers


Affiliations
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, India
2 School of Mechanical and Building Sciences, Christ College of Engineering and Technology, India
     

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These days social communication networks become a part of the daily activity and the users of the social media also increased. The increasing use of social networks by their users leads to large amount of user communication data. And the popularity of social media causes cyberbullying and it became the major problem in communication through online. Cyberbullying leads to many severe problems, undesirable effect on human’s life and it also lead to suicides. In the existing system the unique information such as network, activity, user and tweet contents are extracted from Twitter. By the use of extracted information the cyberbullying words present in the tweet contents are detected using machine learning algorithms like Naïve Bayes, Random Forest, Support Vector Machine and KNN. In the proposed work the rumor tweets and cyberbully tweets are detected, along with these the cyberbully words in the tweet comments also detected using Random Forest and Naïve Bayes classifiers. The required information’s such as name, gender and age of the cyberbully tweeted persons are detected. By the use of twitter speech act classification features along with the machine learning classifiers, the rumor tweets are detected in this proposed work.


Keywords

Cyberbullying Detection, Data Preprocessing, Machine Learning Algorithms, Twitter, Feature Extraction, Rumor Detection.
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  • A. Saravanaraj, J. I. Sheeba, S. Pradeep Devaneyan, Automatic Detection of Cyberbullying from Twitter, ISSN: 2249-9555 Vol.6, No.6, Nov-Dec 2016, pp. 26-31, IJCSITS.

  • Rui Zhao, Anna Zhou, Kezhi Mao, Automatic Detection of Cyberbullying on Social Networks based on Bullying Features, ICDCN ’16 Article No. 43, January 2016, ACM.

  • Chen, Ying, Yilu Zhou, Sencun Zhu, and Heng Xu. "Detecting offensive language in social media to protect adolescent online safety." In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom), pp. 71-80. IEEE, 2012.

  • Qiao Zhang, Shuiyuan Zhang, Jian Dong, Jinhua Xiong, and Xueqi Cheng. Automatic Detection of Rumor on Social Network, pp. 113–122, Springer (2015).

  • SardarHamidian and Mona Diab. Rumor Detection and Classification for Twitter Data, IARIA (2015), 71-77, SOTICS 2015: The Fifth International Conference on Social Media Technologies, Communication, and Informatics, ISBN: 978-1-61208-443-5.

  • Dadvar, M., Trieschnigg, D., Ordelman, R., & de Jong, F. (2013). Improving cyberbullying detection with user context. In Advances in information retrieval (pp. 693-696). Springer.

  • Mohammed Ali Al-garadi, KasturiDewiVarathan, Sri Devi Ravana. Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network, Computers in Human Behavior 63 (2016) 433-443, Elsevier.

  • Nalini, K., & Sheela, L. J. (2015). Classification of Tweets using text classifier to detect cyber bullying. In Emerging ICT for bridging the future-Proceedings of the 49th Annual convention of the Computer Society of India CSI (Vol. 2, pp. 637-645). Springer.

  • Chavan, V. S., &Shylaja, S. (2015). Machine learning approach for detection of cyber aggressive comments by peers on social media network. In Advances in computing, communications and informatics (ICACCI), 2015 International Conference on (pp. 2354-2358). IEEE.

  • J.I. Sheeba and A.Habiba, "Sentence Abusive Detection using Text Mining." CiiT International Journal of Data Mining and Knowledge Engineering, Vol 5. No.7, ISSN (Online): 0974 – 9578, ISSN (Print):0974-9683, 2013 pp: 288-291, 2013.

  • J.I.Sheeba and K.Vivekanandan, “Analysis Of Different Similarity Functions with Fuzzy C-Means Clustering Approach Using Meeting Transcripts”, CiiT International Journal of Data Mining and Knowledge Engineering, Vol. 6, No7, ISSN (Online): 0974 – 9578,ISSN (Print):0974-9683, pp.311-315, October 2014

  • X. Zhao and J. Jiang. An empirical comparison of topics in twitter and traditional media. Singapore Management University School of Information Systems Technical paper series. Retrieved November, 10:2011, 2011.

  • Vosoughi, Soroush, and Deb Roy. Tweet acts: A speech act classifier for twitter. Ar Xiv preprint arXiv: 1605. 05156 (2016).

  • Sanchez, Huascar, and Shreyas Kumar. "Twitter bullying detection." ser. NSDI 12 (2011): 15-15.

  • B.Sri Nandhini, J.I.Sheeba,: Cyberbullying Detection and Classification Using Information Retrieval Algorithm. In: ICARCSET '15, ACM, pp.1-5 (2015)

  • B.Sri Nandhini, J.I.Sheeba,: Online Social Network Bullying Detection Using Intelligence Techniques. In: Procedia Computer Science 45 (2015) 485 – 492, Elsevier, pp.1-8 (2015)

  • J.I.Sheeba, K.Vivekanandan,: Detection of Online Social Cruelty Attack from Forums. In: International InJournal of Data Mining and Emerging Technologies DOI: 10.5958/2249- 3220.2014.00003.2, IndianJournals.com, pp.1-11 ( 2015)

  • Sheeba,J. I., & Devaneyan, S. P.. : Cyberbully Detection Using Intelligent Techniques. International Journal of Data Mining And Emerging Technologies, 6(2), pp. 86-94 (2016)

  • Tata Prathyusha, R. Hemavathy and Dr.J.I.Sheeba,: Cyberbully Detection Using Hybrid Techniques. In: International Conference on Telecommunication, Power Analysis and Computing Techniques (ICTPACT -2017) IEEE, pp.1-6 (2017)


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  • Cyberbully Detection from Twitter Using Classifiers

Abstract Views: 249  |  PDF Views: 6

Authors

J. I. Sheeba
Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry-605014, India
S. Pradeep Devaneyan
School of Mechanical and Building Sciences, Christ College of Engineering and Technology, India

Abstract


These days social communication networks become a part of the daily activity and the users of the social media also increased. The increasing use of social networks by their users leads to large amount of user communication data. And the popularity of social media causes cyberbullying and it became the major problem in communication through online. Cyberbullying leads to many severe problems, undesirable effect on human’s life and it also lead to suicides. In the existing system the unique information such as network, activity, user and tweet contents are extracted from Twitter. By the use of extracted information the cyberbullying words present in the tweet contents are detected using machine learning algorithms like Naïve Bayes, Random Forest, Support Vector Machine and KNN. In the proposed work the rumor tweets and cyberbully tweets are detected, along with these the cyberbully words in the tweet comments also detected using Random Forest and Naïve Bayes classifiers. The required information’s such as name, gender and age of the cyberbully tweeted persons are detected. By the use of twitter speech act classification features along with the machine learning classifiers, the rumor tweets are detected in this proposed work.


Keywords


Cyberbullying Detection, Data Preprocessing, Machine Learning Algorithms, Twitter, Feature Extraction, Rumor Detection.

References