Social Media Cyberbullying Detection using Machine Learning

Hani, John; Mohamed, Nashaat; Mostafa, Ahmed E.; Emad, Zeyad; Amer, Eslam; Mohammed, Ammar

doi:10.14569/ijacsa.2019.0100587

Cited by 85 publications

(51 citation statements)

References 23 publications

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“…Most recently, Lee et al [12] investigated the efficacy of traditional machine learning and neural networks-based models at detecting abusive language on a Twitter dataset. Hani et al [13] extended the work of Reynolds et al [24] at detecting cyberbullying events in text messages from Formspring.me by introducing a set of new classifiers. Such a group of works exposes the scientific efforts made to detect cyberbullying from textual data written in the English language.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, typical cyberbullying detection approaches employ text analysis subtasks such as pre-processing, feature extraction, feature selection, and classification to identify online harassing events. Despite such a well-defined pipeline, there exist very few works in the literature aiming at detecting cyberbullying in textual data from social media written in other languages different from the English language [10][11][12][13]. Furthermore, there are a limited number of works trying to solve the automatic cyberbullying detection problem in Spanish languages [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Chavez¹,

Marianne²

2020

IJACSA

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Cyberbullying is a social problem in which bullies' actions are more harmful than in traditional forms of bullying as they have the power to repeatedly humiliate the victim in front of an entire community through social media. Nowadays, multiple works aim at detecting acts of cyberbullying via the analysis of texts in social media publications written in one or more languages; however, few investigations target the cyberbullying detection in the Spanish language. In this work, we aim to compare four traditional supervised machine learning methods performances in detecting cyberbullying via the identification of four cyberbullying-related categories on Twitter posts written in the Peruvian Spanish language. Specifically, we trained and tested the Naive Bayes, Multinomial Logistic Regression, Support Vector Machines, and Random Forest classifiers upon a manually annotated dataset with the help of human participants. The results indicate that the best performing classifier for the cyberbullying detection task was the Support Vector Machine classifier.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Chavez¹,

Marianne²

2020

IJACSA

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“…In addition, the results show that most common investigated feature is Bigram, Trigram. The commonly used methods for measuring the accuracy are same to other languages (Precession, Recall, F1).Also, the highest accuracy is archived by using SVM classifier that represented with bigram, trigram,4-gram features in [21].…”

Section: Cyberbullying Detection In Latin Languagementioning

confidence: 99%

“…Authors in this work tested the proposed model in three different datasets and platforms. The performance range for each classifier is covered in table 2.The authors in[21], adopted a supervised approach for cyberbullying detection. Authors used different machine learning classifiers, TFIDF and sentiment analysis algorithms for features extraction.…”

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confidence: 99%

Text Mining Techniques for Cyberbullying Detection: State of the Art

Bayari¹

2021

Adv. sci. technol. eng. syst. j.

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The dramatic growth of social media during the last years has been associated with the emergence of a new bullying types. Platforms such as Facebook, Twitter, YouTube, and others are now privileged ways to disseminate all kinds of information. Indeed, communicating through social media without revealing the real identity has emerged an ideal atmosphere for cyberbullying, where people can pour out their hatred. Therefore, become very urgent to find automated methods to detect cyberbullying through text mining techniques. So, many researchers have recently investigated various approaches, and the number of scientific studies about this topic is growing very rapidly. Nonetheless, the methods are used to classify the phenomenon and evaluation methods are still under discussion. Subsequently, comparing the results between the studies and identifying their performance is still difficult. Therefore, the current systematic review has been conducted with the aim of survey the researches and studies that have been conducted so far by the research community in the topic of cyberbullying classification based on text language. In order to direct future studies on the topic to a more consistent and compatible perspective on recent works, we undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field. We made a choice to focus more on techniques that adopted neural networks and machine learning algorithms. After conducting systematic searches and applying the inclusion criteria, 16 different studies were included. It was found that the best accuracy was achieved when a deep learning approach is used particularly CNN approach. It was found also that, SVM is the most common classifier in both Arabic and Latin languages and outperformed the other classifiers. Also, the most widely used feature is N-Gram especially bigram and trigram. Furthermore, results show that Twitter is the main source for the collected datasets, and there are no unified datasets. There is also a shortage of studies in Arabic texts for cyberbullying identification in contrast with English texts.

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“…In [6] proposes a machine learning approach to detect and prevent cyberbullying using machine learning techniques. Evaluation of the proposed approach to the cyberbullying dataset shows that the neural network works best achieving 92.8% accuracy and support vector machines reaches 90.3.…”

Section: Related Workmentioning

confidence: 99%

Level of Budget Execution According to the Professional Profile of Regional Governors Applying Machine Learning Models

Rocha¹,

Aurelio²,

Irazema³

et al. 2020

IJACSA

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Machine Learning is a discipline of artificial intelligence that implements computer systems capable of learning complex patterns automatically and predicting future behaviors. The objective was to implement a Machine Learning model that allows to identify, classify and predict the influence of the professional training of the governors in the execution of the public spending of the regional governments of Peru. Of the 14 indicators of academic training, professional experience and university studies were selected as significant indicators that contribute to the execution of public spending by the 25 governors of Peru. For the prediction of the execution of the public spending of the regional governors, a supervised learning algorithm was implemented. The mean square error for the Machine Learning regression model was 4.20 and the coefficient of determination was 0.726, which indicates that the execution of public spending by regional governments is explained with 72.6% by the professional experience and university studies of the governors. The regional governors of Peru with university studies and professional experience achieve better results in the execution of public spending in the regional governments of Peru.

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Social Media Cyberbullying Detection using Machine Learning

Cited by 85 publications

References 23 publications

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Text Mining Techniques for Cyberbullying Detection: State of the Art

Level of Budget Execution According to the Professional Profile of Regional Governors Applying Machine Learning Models

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