Corpus ID: 86867275

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

  title={Cyberbullying Detection based on Semantic- Enhanced Marginalized Denoising Auto-Encoder},
  author={Dasari Swathi and Sadhu Ratna Babu},
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… Expand

Figures from this paper

Now-adays internet is mostly useful for the people for school, work, and social use, so too do more people turn to the Internet to take out their frustrations and aggression. One form of cyberExpand
Bullying Hurts: A Survey on Non-Supervised Techniques for Cyber-bullying Detection
Recent research on non-supervised techniques in textual-based cyber-bullying detection including detecting roles, detecting emotional state, automated annotation and stylometric methods are surveyed and some suggestions for future research are offered. Expand
Robust Detection of Cyberbullying in Social Media
This PhD thesis focuses on a novel formulation of the online classification problem as sequential hypothesis testing that seeks to drastically reduce the number of features used while maintaining high classification accuracy, and seeks to develop efficient semisupervised methods that extrapolate from a small seed set of expert annotations. Expand
A Study of Cyberbullying Detection Using Machine Learning Techniques
  • S. Kargutkar, V. Chitre
  • Computer Science
  • 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)
  • 2020
A system is proposed to give a double characterization of cyberbullying, utilizing an inventive idea of CNN for content examination and finding a guileless way to deal with furnish the arrangement with less precision. Expand
Cyberbullying Ends Here: Towards Robust Detection of Cyberbullying in Social Media
This work proposes a sequential hypothesis testing formulation that seeks to drastically reduce the number of features used in classifying each comment while maintaining high classification accuracy, and introduces CONcISE, a novel approach for timely and accurate Cyberbullying detectiON on Instagram media SEssions. Expand
A Detailed Survey On Cyberbullying in Social Networks
  • V. Krithika, V. Priya
  • Computer Science
  • 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE)
  • 2020
The purpose of this survey is to explore the various research works performed for detection and prevention on Cyberbullying by observing the drawbacks of the existing research. Expand
Review of Machine Learning methods for Identification of Cyberbullying in Social Media
  • Neha Singh, S. Sharma
  • 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)
  • 2021
In the modern era, the usage of internet has increased tremendously which in turn has led to the evolution of large amount of data. Cyber world has its own pros and cons. One of the alarmingExpand
Approaches to Automated Detection of Cyberbullying: A Survey
This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field. Expand
Optimal Online Cyberbullying Detection
This work proposes a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made, and shows that the approach is highly scalable while not sacrificing accuracy for scalability. Expand
Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data
This research focuses on cyberbullying detection in the code-mix data, specifically the Hinglish, which refers to the juxtaposition of words from the Hindi and English languages, and proposes MIIL-DNN, a multi-input integrative learning model based on deep neural networks. Expand


Online Social Network Bullying Detection Using Intelligence Techniques
The detection method can identify the presence of cyberbullying terms and classify cyberbullies activities in social network such as Flaming, Harassment, Racism and Terrorism, using Fuzzy logic and Genetic algorithm. Expand
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
A set of unique features derived from Twitter; network, activity, user, and tweet content, based on these feature, a supervised machine learning solution for detecting cyberbullying in the Twitter network is developed. Expand
An Effective Approach for Cyberbullying Detection
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. Expand
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
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. Expand
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 onExpand
Issues and Challenges of Cyber Security for Social Networking Sites (Facebook)
A survey is conducted to find users view regarding security and privacy of social networking sites and regarding default privacy setting improvement particularly Facebook. Expand
A Normative Agent System to Prevent Cyberbullying
  • T. Bosse, S. Stam
  • Computer Science, Political Science
  • 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
  • 2011
The results show that the normative agents have the potential to reduce the amount of norm violations on the long term. Expand
Improving Cyberbullying Detection with User Context
It is shown that taking user context into account improves the detection of cyberbullying. Expand
Cyber Crime: Critical View
The contribution of this research paper is an overview on cyber crime and the ethical issues related to this field. Centre of focus are the issues connected to the massive increase in cyber crimeExpand
Automatic Detection of Cyberbullying from Twitter
  • International Journal of Computer Science and Information Technology & Security
  • 2016