Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

Vyawahare, Madhura; Chatterjee, Mainak

doi:10.1007/978-981-15-0132-6_3

Cited by 26 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic detection of cyberbullying became the subject of many studies over the past few years [1,4,5,9,13,14]. However, cyberbullying has dramatically increased recently [2,15].…”

Section: Introductionmentioning

confidence: 99%

“…However, cyberbullying has dramatically increased recently [2,15]. Different types of cyberbullying were investigated and many tools and techniques were used to analyze the text-content and detect the cyberbullying phenomenon [13]. Natural language processing (NLP) techniques were commonly used to process the text data to extract useful patterns for detecting cyberbullying [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Alhashmi¹,

Darem²

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Due to the proliferation of internet-enabled smartphones, many people, particularly young people in Arabic society, have widely adopted social media platforms as a primary means of communication, interaction and friendship making. The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world. However, such networks expose young people to cyberbullying and offensive content that puts their safety and emotional well-being at serious risk. Although, many solutions have been proposed to automatically detect cyberbullying, most of the existing solutions have been designed for English speaking consumers. The morphologically rich languages-such as the Arabic language-lead to data sparsity problems. Thus, render solutions developed for another language are ineffective once applied to the Arabic language content. To this end, this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model. A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected from five different platforms. The outputs of the selected classifiers are combined using consensus-based decisionmaking in which the F1-Score of each classifier was used to rank the classifiers. Then, the Sigmoid function, which can reproduce human-like decision making, is used to infer the final decision. The outcomes show the efficacy of the proposed model comparing to the other studied classifiers. The overall improvement gained by the proposed model reaches 1.3% comparing with the best trained classifier. Besides its effectiveness for Arabic language content, the proposed model can be generalized to improve cyberbullying detection in other languages.

show abstract

“…Automatic detection of cyberbullying became the subject of many studies over the past few years [1,4,5,9,13,14]. However, cyberbullying has dramatically increased recently [2,15].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Alhashmi¹,

Darem²

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…In Ref. [11], a classification of cyber-bullying detection methods in online social networks was presented; it shows a survey of techniques to automatically identify cyber-bullying through the machine learning algorithms. Another interesting approach is MANDOLA [12]; it is a big-data processing system intended to evaluate the proliferation and effect of online hate-related speech, which is generally inspired by religion beliefs, ethnicity or gender.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

et al. 2021

View full text Add to dashboard Cite

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

show abstract

“…Because a cyber bully (cyber stalker) may be identified directly once they send annoying communications to a victim, cyber stalking is less severe than other types [11]. Many studies have demonstrated that ML algorithms can be used to predict and identify cyberbullying behaviour [12]. Because enormous amounts of data are created every second, methods may be trained efficiently.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis and Deep Learning Based Cyber Bullying Detection in Twitter Dataset

T.T¹,

Jeetha²

2021

IJRTE

View full text Add to dashboard Cite

When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual's reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.

show abstract

Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

Cited by 26 publications

References 30 publications

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Sentiment Analysis and Deep Learning Based Cyber Bullying Detection in Twitter Dataset

Contact Info

Product

Resources

About