Cyberbullying Detection and Classification Using Information Retrieval Algorithm

Nandhini, B. Sri; Sheeba, J.I.

doi:10.1145/2743065.2743085

Cited by 62 publications

(31 citation statements)

References 8 publications

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“…Researchers can use these values as a guide when conducting experiments using comparable datasets. Thus the best results achieved for binary classification using a social network corpus was by Nandhini and Sheeba (2015b). Likewise, Chen et al (2012) achieved the best role identification scores for media platform type corpora.…”

Section: Performance Comparisonmentioning

confidence: 89%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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Abstract-Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. 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.

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Section: Performance Comparisonmentioning

confidence: 89%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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“…These works have largely applied a text analysis approach to online comments, since this approach results in higher precision and lower false positives than simpler list-based matching of profane words [47]. Previous research [33,34,24,31] applied text based cyberbullying on Formspring.me and Myspace dataset. Dinakar et al investigated both explicit and implicit cyberbullying by analyzing negative text comments on YouTube and Formspring profiles [9].…”

Section: Related Workmentioning

confidence: 99%

Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network

Hosseinmardi

Mattson

Rafiq

et al. 2015

Lecture Notes in Computer Science

216

204

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Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect and predict incidents of cyberbullying in Instagram, a media-based mobile social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, profanity, social graph features, temporal commenting behavior, linguistic content, and image content. Using the labeled data, we further design and evaluate the performance of classifiers to automatically detect and predict incidents of cyberbullying and cyberaggression.

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“…There are many approaches that proposes systems which can detect cyberbullying automatically with high accuracy. First one is author Nandhini et al [3] have proposed a model that uses Naïve Bayes machine learning approach and by their work they achieved 91% accuracy and got their dataset from MySpace.com, and then they proposed another model [4] Naïve Bayes classifier and genetic operations (FuzGen) and they achieved 87% accuracy. Another approach by Romsaiyud et al [5] they enhanced the Naïve Bayes classifier for extracting the words and examining loaded pattern clustering and by this approach they achieved 95.79% accuracy on datasets from Slashdot, Kongregate, and MySpace.…”

Section: Related Workmentioning

confidence: 99%

Social Media Cyberbullying Detection using Machine Learning

Hani¹,

Mohamed²,

Mostafa³

et al. 2019

IJACSA

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With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Social networks provides a rich environment for bullies to uses these networks as vulnerable to attacks against victims. Given the consequences of cyberbullying on victims, it is necessary to find suitable actions to detect and prevent it. Machine learning can be helpful to detect language patterns of the bullies and hence can generate a model to automatically detect cyberbullying actions. This paper proposes a supervised machine learning approach for detecting and preventing cyberbullying. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed approach on cyberbullying dataset shows that Neural Network performs better and achieves accuracy of 92.8% and SVM achieves 90.3. Also, NN outperforms other classifiers of similar work on the same dataset.

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Cyberbullying Detection and Classification Using Information Retrieval Algorithm

Cited by 62 publications

References 8 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

Analyzing Labeled Cyberbullying Incidents on the Instagram Social Network

Social Media Cyberbullying Detection using Machine Learning

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