Online Social Network Bullying Detection Using Intelligence Techniques

  title={Online Social Network Bullying Detection Using Intelligence Techniques},
  author={B. Sri Nandhini and J. I. Sheeba},
  journal={Procedia Computer Science},

Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

A broad survey of all recent techniques proposed by researchers for cyberbullying detection and prediction is done and a comparative analysis and classification of the work done in recent years is presented.

Cyber Bullying Detection and Classification using Multinomial Naïve Bayes and Fuzzy Logic

A machine learning based approach is proposed to detect cyber bullying activities from social network data and results show that the accuracy of the classifier for considered data set is 88.76%.

Cyberbullying Detection Using Machine Learning

A machine learning-based approach is proposed to detect cyberbullying activities from social network data and it is revealed that the accuracy of the proposed approach increases with more classification data.

Cyberbullying Detection on Social Network Services

Three data mining techniques, k-nearest neighbors, support vector machine, and decision tree are used in this study to detect the cyberbullying tweets and select the best performance model for cyberbullies prediction.

Towards the detection of cyberbullying based on social network mining techniques

An approach based on social networks analysis and data mining for cyberbullying detection is proposed, including keyword matching technique, opinion mining and social network analysis.

Cyberbullying Detection Through Sentiment Analysis

  • J. Atoum
  • Computer Science
    2020 International Conference on Computational Science and Computational Intelligence (CSCI)
  • 2020
A SA model for identifying cyberbullying texts in Twitter social media is proposed and encouraging outcomes are shown when a higher n-grams language model is applied on such texts in comparison with similar previous research.

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

This work is a systematic literature review to gather, explore, comprehend and analyze the research trends, gaps and prospects of this alliance of using soft computing techniques for cyberbullying detection on social multimedia using a meta-analytic approach.

Cyberbully Detection Using Term Weighting Scheme and Naïve Bayes Classifier

The aim of this research is to detect the presence of text cyberbullying from online post by comparing two term weighting schemes and two classification algorithms, and shows that Naive Bayes classifier yields a better accuracy when used with TF-IDF which is 97.60%.

Automatic detection of cyberbullying in social media text

This paper describes the collection and fine-grained annotation of a cyberbullying corpus for English and Dutch and performs a series of binary classification experiments to determine the feasibility of automatic cyberbullies detection.



Improved cyberbullying detection using gender information

It is demonstrated that taking gender-specific language features into account improves the discrimination capacity of a classifier to detect cyberbullying.

Using Machine Learning to Detect Cyberbullying

This project uses data collected from the website, a question-and-answer formatted website that contains a high percentage of bullying content, to train a computer to recognize bullying content and develops rules to automatically detect cyber bullying content.

Learning from Bullying Traces in Social Media

Evidence is presented that social media, with appropriate natural language processing techniques, can be a valuable and abundant data source for the study of bullying in both worlds.

Modeling the Detection of Textual Cyberbullying

This work decomposes the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems and shows that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.

Cyberbullying: its nature and impact in secondary school pupils.

Two studies found cyberbullying less frequent than traditional bullying, but appreciable, and reported more outside of school than inside, and being a cybervictim, but not a cyberbully, correlated with internet use.

Learning to Identify Internet Sexual Predation

This work integrates communication theories and computer science algorithms to create a program that can detect the occurrence of sexual predation in an online social setting and uses machine learning algorithms to classify posts.

Cyber Bullying: An Old Problem in a New Guise?

  • M. Campbell
  • Political Science
    Australian Journal of Guidance and Counselling
  • 2005
Abstract Although technology provides numerous benefits to young people, it also has a ‘dark side’, as it can be used for harm, not only by some adults but also by the young people themselves.