Classification of Cyber-Aggression Cases Applying Machine Learning

Gutiérrez-Esparza, Guadalupe O.; Vallejo-Allende, Maité; Hernández-Torruco, José

doi:10.3390/app9091828

Cited by 27 publications

(18 citation statements)

References 37 publications

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“…Similarly, Molina-Gonzáles et al [15] proposed an ensemble of supervised classifiers to identify offensive messages on the 2019 edition of MEX-A3T. Gutiérrez-Esparza et al [16] developed a classification model to detect cyberbullying events (i.e., racism, violence based on sexual orientation, and violence against women) on a Mexican-Spanish textual dataset collected from Facebook. The authors highlight the participation of school professors and psychologists, with experience in evaluation and intervention in cases of bullying, during the annotation process.…”

Section: Related Workmentioning

confidence: 99%

“…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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“…JRip, C4.5, and Linear SVM are known for getting good results in classification tasks [28][29][30]. JRip and C4.5 also provide predictive models, understandable by humans.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

et al. 2020

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Metabolic syndrome is a health condition that increases the risk of heart diseases, diabetes, and stroke. The prognostic variables that identify this syndrome have already been defined by the World Health Organization (WHO), the National Cholesterol Education Program Third Adult Treatment Panel (ATP III) as well as by the International Diabetes Federation. According to these guides, there is some symmetry among anthropometric prognostic variables to classify abdominal obesity in people with metabolic syndrome. However, some appear to be more sensitive than others, nevertheless, these proposed definitions have failed to appropriately classify a specific population or ethnic group. In this work, we used the ATP III criteria as the framework with the purpose to rank the health parameters (clinical and anthropometric measurements, lifestyle data, and blood tests) from a data set of 2942 participants of Mexico City Tlalpan 2020 cohort, applying machine learning algorithms. We aimed to find the most appropriate prognostic variables to classify Mexicans with metabolic syndrome. The criteria of sensitivity, specificity, and balanced accuracy were used for validation. The ATP III using Waist-to-Height-Ratio (WHtR) as an anthropometric index for the diagnosis of abdominal obesity achieved better performance in classification than waist or body mass index. Further work is needed to assess its precision as a classification tool for Metabolic Syndrome in a Mexican population.

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“…e damage states of this study are based on the in situ inspections and can obtain much information about bridges quickly. e RF algorithm was proposed by Breiman [37] and had been proven to achieve suitable results in the application of feature selection [38]. e RF algorithm was chosen as the classifier to assess the importance of nine features, and the Classification and Regression Tree (CART) algorithm was applied to classify the data [14].…”

Section: Default Processingmentioning

confidence: 99%

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

Jia

Jun

Liu

2020

Advances in Civil Engineering

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Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction.

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Classification of Cyber-Aggression Cases Applying Machine Learning

Cited by 27 publications

References 37 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

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

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