Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis

@article{Kumar2019CyberbullyingDO,
  title={Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis},
  author={Akshi Kumar and Nitin Sachdeva},
  journal={Multimedia Tools and Applications},
  year={2019},
  pages={1-38}
}
Cyberbullying is to bully someone in the digital realm. It has become extremely detrimental as the social media and the internet have become more popular and omnipresent. People use the internet services to viciously attack others from behind a screen. The substantial growth in the dimensionality, heterogeneity, subjectivity and multimodality of social media and the pressing need to timely curtail the damage instigated through cyberbullying, has fostered the need to devise automated mechanisms… 
Cyberbullying Checker: Online Bully Content Detection Using Hybrid Supervised Learning
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  • Computer Science
    International Conference on Intelligent Computing and Smart Communication 2019
  • 2019
TLDR
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TLDR
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  • V. V, H. D
  • Computer Science
    INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH
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This paper proposes a detailed review on machine and deep learning approach for detecting and preventing multimodal and multilingual cyberbullying.
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The design, construction and evaluation of annotated Arabic cyberbullying corpus
TLDR
The design, construction, and evaluation of a multi-dialect, annotated Arabic Cyberbullying Corpus (ArCybC), a valuable resource for Arabic CB detection and motivation for future research directions in Arabic Natural Language Processing (NLP), are discussed.
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References

SHOWING 1-10 OF 59 REFERENCES
Cyberbullying detection and prevention: Data mining and psychological perspective
  • Sourabh Parime, Vaibhav Suri
  • Computer Science
    2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]
  • 2014
TLDR
This paper focuses on the data mining and machine learning techniques which have been proposed to detect and prevent cyberbullying and implements one such machine learning technique to identify the presence or absence of cyberbullies using the dataset from a popular social networking website.
Automatic detection of cyberbullying on social networks based on bullying features
TLDR
A representation learning framework specific to cyberbullying detection is proposed, which expands a list of pre-defined insulting words and assigns different weights to obtain bullying features, which are concatenated with Bag-of-Words and latent semantic features to form the final representation before feeding them into a linear SVM classifier.
Cyberbullying Detection based on Semantic- Enhanced Marginalized Denoising Auto-Encoder
TLDR
The technique for detection and avoidance of cyberbullying words in social media when cyberbullies takes place is proposed and also the technique for Detection and blocking the accessing of a predator in social social media is proposed.
Prediction of cyberbullying incidents in a media-based social network
TLDR
This paper investigates the prediction of cyberbullying incidents in Instagram, a popular media-based social network, and extracts several important features from the initial posting data for automated cyberbullies prediction, including profanity and linguistic content of the text caption, image content, as well as social graph parameters and temporal content behavior.
Content based approach to find the credibility of user in social networks: an application of cyberbullying
TLDR
This paper first categorizes the messages into direct and indirect bullying messages and then proceeds to find the solution for controlling the bullying through checking the credibility of user.
Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network
TLDR
This research paper performs a thorough investigation of cyberbullying instances in Vine, a video-based online social network, and trains different classifiers based upon the labeled media sessions to detect instances of cyber Bullying.
Cyberbullying Detection with Weakly Supervised Machine Learning
  • Elaheh Raisi, Bert Huang
  • Computer Science
    2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2017
TLDR
A machine learning method for simultaneously inferring user roles in harassment-based bullying and new vocabulary indicators of bullying, and evaluating PVC on three social media data sets, demonstrating quantitatively and qualitatively its effectiveness in cyberbullying detection.
Approaches to Automated Detection of Cyberbullying: A Survey
TLDR
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.
...
...