A Proposed Framework to Analyze Abusive Tweets on the Social Networks

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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


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


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