Classification of Cyber-Aggression Cases Applying Machine Learning

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a...

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Bibliographic Details
Published in:Applied sciences 2019-05, Vol.9 (9), p.1828
Main Authors: Gutiérrez-Esparza, Guadalupe Obdulia, Vallejo-Allende, Maite, Hernández-Torruco, José
Format: Article
Language:English
Subjects:
Aggression
Aggressive behavior
Artificial intelligence
Bullying
Classification
Computer science
cyber-aggression
Cyberbullying
Data mining
Datasets
Entropy
Gays & lesbians
Guillain-Barre syndrome
Hate crimes
International conferences
Internet
Learning algorithms
Machine learning
racism
random forest
sentiment analysis
Social networks
Social organization
Society
Spanish language
Violence
violence against women
violence based on sexual orientation
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Summary:The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9091828