Cyberbullying classification using text mining

Noviantho,; Isa, Sani Muhamad; Ashianti, Livia

doi:10.1109/icicos.2017.8276369

Cited by 39 publications

(14 citation statements)

References 4 publications

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“…In [28], Noviantho et al obtained a dataset from Kaggle, which contains ~12K tweets out of which 1068 were bullying ones. In their approach, they used machine learning techniques such as SVM and naïves along with N gram technique.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

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“…Both methods are used to learn the usage context of a word. Global vector space [20], [25], [26], [27], [28], [29], [30] Distance Measures Edit-Distance [20] Word Embedding Word2Vec [31], [32], [28], [33], [34], [35], [30], [36], [37], [38], [39] Skip-gram [25] CBoW [25], [32] BoW [40], [31], [33], [34] TF-IDF [26], [27] FastText [25], [36] GLoVe [41], [42], [37] LSHWE [37] Vulgarity/Hate Features [43], [25], [32], [33], [44] Sentiment Sentiment Analysis [27], [45], [32], [33], [41], [46], [30], [39] User Profile [27],…”

Section: Word Embedding Techniquesmentioning

confidence: 99%

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Arif

2021

JISCR

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Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

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“…The study concluded that it was difficult to classify cyberbullying due to the data sparseness and the degree to which the categories are lexicalized. Similarly, [13] proposed a classification model with optimal accuracy for identifying cyberbully conversation in social media platform known as Formspring.me implementing Naive Bayes method and Support Vector Machine (SVM), and later applied n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. The result yields an average accuracy of 92.81% through the Naive Bayes and the SVM with a poly kernel yields an average accuracy of 97.11%.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Cyber Bullying on Facebook Using Text Mining

Aliyu

Dogo

Ajibade

et al. 2020

J. Appl. Artif. Intell.

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Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.

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Cyberbullying classification using text mining

Cited by 39 publications

References 4 publications

Multilingual Cyberbullying Detection System

Multilingual Cyberbullying Detection System

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Analysis of Cyber Bullying on Facebook Using Text Mining

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