Citation

Singh A, Kaur M. IJITEE 2019; 8(8): 1193-1207.

Copyright

(Copyright © 2019, Blue Eyes Intelligence Engineering and Sciences Publication)

DOI

unavailable

PMID

unavailable

Abstract

In the recent past, the issues of Content-based Cybercrime have gained considerable attention. Social media providers seek for accurate and efficient way of recognizing offensive content for shielding their users. Content-based Cybercrime detection is one of the conspicuous area of data mining that deals with the recognition and examination of bully contents usually presented at social media. The current work emphasizes on cyberbullying, one of the prominent problems that arose due to the increasing fame of social network and its fast acceptance in our day-to-day survives. The social network provides a convenient platform for the cyber predators to bull their preys especially targeting young youth. In severe cases, the victims have attempted suicide due to humiliation, insult, and hostile messages left by the predators. This work presents a systematic critical study to accumulate, investigate, apprehend and explore the patterns and study gaps in a well-organized manner. The study portrays a comprehensive systematic literature review of strategies proposed in the field of content-based cybercrime. In this review, precise investigation methodology is utilized based on a total selected 27 research papers out of 51 research papers published in preeminent workshops, symposiums and conferences and conspicuous journals. The survey relates to several data preprocessing techniques, content-based feature, machine learning methodology, online social networking datasets and evaluation parameter used in context of detecting content-based cybercrime. This Methodical analysis of the research work acts as an assistant for the researchers to discover the significant characteristics of content-based Cybercrime detection techniques. © BEIESP.

Language: en

Keywords

Cyberbullying; Machine learning; Content based cybercrime; Deep learning