• Corpus ID: 86867275

Cyberbullying Detection based on Semantic- Enhanced Marginalized Denoising Auto-Encoder

@inproceedings{Swathi2018CyberbullyingDB,
  title={Cyberbullying Detection based on Semantic- Enhanced Marginalized Denoising Auto-Encoder},
  author={Dasari Swathi and S. Babu},
  year={2018}
}
Social Networking is a group of Internet based applications that allow the creation and exchange of user-generated content. Via social media, people can enjoy enormous information, convenient communication experience and so on. Since, social media may have some side effects such as cyberbullying, which may have negative impacts on the life of people, especially children and teenagers. Cyberbullying can be defined as aggressive, intentional actions performed by an individual or a group of people… 

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References

SHOWING 1-10 OF 14 REFERENCES
An Effective Approach for Cyberbullying Detection
TLDR
An effective approach to detect cyberbullying messages from social media through a weighting scheme of feature selection is proposed and a graph model is presented to extract the cyberBullying network, which is used to identify the most active cyberbullies predators and victims through ranking algorithms.
Detecting Offensive Language in Social Media to Protect Adolescent Online Safety
  • Ying Chen, Yilu Zhou, Sencun Zhu, Heng Xu
  • Computer Science
    2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
  • 2012
TLDR
This work proposes the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media, and incorporates a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content.
Impact of Usage of Social Networking Sites on Youth: An Overview
Social Networking Sites (SNS) is a buzz word in today’s world due to its enormous growth, customer base and usage. The main focus of this paper is to present an insight into impact of SNS usage on
Issues and Challenges of Cyber Security for Social Networking Sites (Facebook)
TLDR
A survey is conducted to find users view regarding security and privacy of social networking sites and regarding default privacy setting improvement particularly Facebook.
A Normative Agent System to Prevent Cyberbullying
  • T. Bosse, S. Stam
  • Computer Science
    2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
  • 2011
TLDR
The results show that the normative agents have the potential to reduce the amount of norm violations on the long term.
Improving Cyberbullying Detection with User Context
TLDR
It is shown that taking user context into account improves the detection of cyberbullying.
Cyber Crime: Critical View
TLDR
An overview on cyber crime and the ethical issues related to this field and the issues connected to the massive increase in cyber crime ratio are reviewed.
J.I.Sheeba, “Online Social Network Bullying Detection
  • Using Intelligence Techniques”,
  • 2015
...
...