A Proposed Framework to Analyze Abusive Tweets on the Social Networks

Full Text (PDF, 918KB), PP.46-56

Views: 0 Downloads: 0

Author(s)

Priya Gupta 1,* Aditi Kamra 2 Richa Thakral 2 Mayank Aggarwal 2 Sohail Bhatti 2 Vishal Jain 3

1. Department of Computer Science, Maharaja Agrasen College, University of Delhi, Delhi, India

2. Maharaja Agrasen College, University of Delhi, Delhi, India

3. Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), Delhi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2018.01.05

Received: 22 Sep. 2017 / Revised: 15 Nov. 2017 / Accepted: 6 Dec. 2017 / Published: 8 Jan. 2018

Index Terms

Twitter, Classifier, Detection, Semantic, Syntactic, Abusive, Data-Cleaning, Classification

Abstract

This paper takes Twitter as the framework and intended to propose an optimum approach for classification of Twitter data on the basis of the contextual and lexical aspect of tweets. It is a dire need to have optimum strategies for offensive content detection on social media because it is one of the most primary modes of communication, and any kind of offensive content transmitted through it may harness its benefits and give rise to various cyber-crimes such as cyber-bullying and even all content posted during the large even on twitter is not trustworthy. In this research work, various facets of assessing the credibility of user generated content on Twitter has been described, and a novel real-time system to assess the credibility of tweets has been proposed by assigning a score or rating to content on Twitter to indicate its trustworthiness. A comparative study of various classifying techniques in a manner to support scalability has been done and a new solution to the limitations present in already existing techniques has been explored.

Cite This Paper

Priya Gupta, Aditi Kamra, Richa Thakral, Mayank Aggarwal, Sohail Bhatti, Vishal Jain, "A Proposed Framework to Analyze Abusive Tweets on the Social Networks", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.1, pp. 46-56, 2018.DOI: 10.5815/ijmecs.2018.01.05

Reference

[1] Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 22.

[2] Boykin, P. O., & Roychowdhury, V. P. (2005). Leveraging social networks to fight spam. Computer, IEEE Computer Magazine, 38(4), 61-68.

[3] Chelmis, C., & Prasanna, V. K. (2011, October). Social networking analysis: A state of the art and the effect of semantics. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on (pp. 531-536). IEEE.

[4] Churcharoenkrung, N., Kim, Y. S., & Kang, B. H. (2005, April). Dynamic Web content filtering based on user's knowledge. In Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on (Vol. 1, pp. 184-188). IEEE.

[5] Frakes, W., Baeza-Yates, R. (eds.) (1992),Information Retrieval: Data Structures & Algorithms, Prentice-Hall

[6] Gavrilis, D., Tsoulos, I., & Dermatas, E. (2006). Neural recognition and genetic features selection for robust detection of e-mail spam. Advances in Artificial Intelligence, 498-501.

[7] Golbeck, J. A. (2005). Computing and applying trust in web-based social networks (Doctoral dissertation).

[8] Kim, Y. H., Hahn, S. Y., & Zhang, B. T. (2000, July). Text filtering by boosting naive Bayes classifiers. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 168-175). ACM.

[9] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). Rcv1: A new benchmark collection for text categorization research. Journal of machine learning research, 5(Apr), 361-397.

[10] Schütze, H. (2008, June). Introduction to information retrieval. In Proceedings of the international communication of association for computing machinery conference.

[11] Venkata S. Lakshmi, K. Hema,(2014) Filtering Information for Short Text Using OSN International Journal of Advanced Research in Computer Science & Technology (IJARCST), 2(2)

[12] Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.

[13] Bobicev, V., & Sokolova, M. (2008, July). An Effective and Robust Method for Short Text Classification. In AAAI (pp. 1444-1445).

[14] Yerazunis, W. S. (2004, January). The spam-filtering accuracy plateau at 99.9% accuracy and how to get past it. In Proceedings of the 2004 MIT Spam Conference.

[15] Zavuschak, I & Burov, Y.(2017), "The Context of Operations as the basis for the Construction of Ontologies of Employment Processes", International Journal of Modern Education and Computer Science(IJMECS), 9(11), 13-24, DOI: 10.5815/ijmecs.2017.11.02

[16] Narinder K. Seera, Vishal Jain,"Perspective of Database Services for Managing Large-Scale Data on the Cloud: A Comparative Study", IJMECS, vol.7, no.6, pp.50-58, 2015.DOI: 10.5815/ijmecs.2015.06.08

[17] Asad Mehmood, Abdul S. Palli, M.N.A. Khan,"A Study of Sentiment and Trend Analysis Techniques for Social Media Content", IJMECS, vol.6, no.12, pp.47-54, 2014.DOI: 10.5815/ijmecs.2014.12.07