Detection of Traits in Students with Suicidal Tendencies on Internet Applying Web Mining

Autor/inn/en	Castillo-Zúñiga, Iván; Luna-Rosas, Francisco-Javier; López-Veyna, Jaime-Iván
Titel	Detection of Traits in Students with Suicidal Tendencies on Internet Applying Web Mining
Quelle	In: Comunicar: Media Education Research Journal, 30 (2022) 71, S.101-112 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Castillo-Zúñiga, Iván) ORCID (Luna-Rosas, Francisco-Javier) ORCID (López-Veyna, Jaime-Iván)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1134-3478
Schlagwörter	Psychological Patterns; Suicide; Web Sites; Student Characteristics; Internet; Information Retrieval; Data Analysis; Models; Prevention; At Risk Students; Bullying; Computer Mediated Communication; Adolescents; Artificial Intelligence; Semantics; Prediction + Suchen Sie Ihr Suchwort? Selbstmord; Web-Design; Auswertung; Analogiemodell; Prävention; Vorbeugung; Mobbing; Computerkonferenz; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Künstliche Intelligenz; Semantik; Vorhersage
Abstract	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. (As Provided).
Anmerkungen	Grupo Comunicar Ediciones. Marina 8, Atico B - 21001 Huelva, Spain. Tel: 34-959-248480; e-mail: info@grupocomunicar.com; Web site: https://www.revistacomunicar.com/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01