Multilingual Cyberbullying Detection System

Pawar, Rohit; Raje, Rajeev R.

doi:10.1109/eit.2019.8833846

Cited by 50 publications

(21 citation statements)

References 18 publications

(27 reference statements)

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“…The authors observed that tweets with similar content written in different languages hinder the classifiers' performances. Pawar and Raje [36] modeled linguistic patterns upon a hand-labeled bilingual (Hindi and Marathi languages) dataset using Machine Learning and Natural Language Processing techniques to detect cyberbullying in Twitter and Internet forums. Moreover, Steimel et al [37] experimented with a general cyberbullying detection model across multiple languages (English and German) with data collected from Twitter.…”

Section: Related Workmentioning

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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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%

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

Chavez¹,

Marianne²

2020

IJACSA

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“…The datasets are usually collected by crawling the target social media using its Application Programming Interface (API). There are also prepared datasets on Kaggle website [44].…”

Section: Datasetsmentioning

confidence: 99%

“…However, there are other datasets which are used in practice, but they are not popular among researchers. Pawar et al [44] used datasets from multiple sources as they realized gathering dataset for Hindi and Marathi language would be a challenge. They obtained dataset from tour reviews, movie reviews and newspaper reviews for Hindi language.…”

Section: Formspringmentioning

confidence: 99%

Cyberbullying Detection using Ensemble Method

Puthenveedu¹

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“…Through a revision of the literature, works were studied that propose some aggression detection mechanism. As has already been mentioned, most of these works were applied to texts in English [8][9][10][11][12][13][14][15][16][17]. Regarding the approach used, more than 50% of the articles used ML techniques, using different corpora to train classifiers.…”

Section: Introductionmentioning

confidence: 99%

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

et al. 2021

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In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.

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Multilingual Cyberbullying Detection System

Cited by 50 publications

References 18 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

Cyberbullying Detection using Ensemble Method

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

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