Detection of traits in students with suicidal tendencies on Internet applying Web Mining

This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The pro...

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Veröffentlicht in:Comunicar Jg. 30; H. 71; S. 101 - 112
Hauptverfasser: Castillo-Zúñiga, Iván, Luna-Rosas, Francisco-Javier, López-Veyna, Jaime-Iván
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Huelva Grupo Comunicar 2022
Grupo Comunicar Ediciones
Ausgabe:English ed.
Schlagworte:
Accuracy
Adolescents
Algorithms
Artificial Intelligence
At Risk Students
Big Data
Bullying
Computer Mediated Communication
Data Analysis
Data mining
Datasets
Deep learning
Dynamic semantics
Genetics
Information Retrieval
Internet
Machine learning
Mining
Mining industry
Natural language processing
Neural networks
Ontology
Prediction
Prevention
Prevention programs
Psychological Patterns
Recurrent
Semantic web
Semantics
Student Characteristics
Students
Suicide
Suicides & suicide attempts
Teenagers
Variables
Web Sites
Youth
ISSN:1134-3478
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Zusammenfassung:This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The procedure considers a Web Scraper to locate and download information from the Internet, as well as Natural Language Processing techniques to retrieve the words. To explore the information, a dataset based on Dynamic Tables and Semantic Ontologies was constructed, specifying the predictive variables in young people with suicidal inclination. Finally, to evaluate the efficiency of the model, Machine Learning and Deep Learning algorithms were used. It should be noticed that the procedures for the construction of the dataset (using Genetic Algorithms) and obtaining the knowledge (using Parallel Computing and Acceleration with GPU) were optimized. The results reveal an accuracy of 96.28% on the detection of characteristics in adolescents with suicidal tendencies, reaching the best result through a Recurrent Neural Network with 98% accuracy. It is inferred that the model is viable to establish bases on mechanisms of action and prevention of suicidal behaviors, which can be implemented in educational institutions or different social actors.
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ISSN:1134-3478
DOI:10.3916/C71-2022-08