Keywords
Suicidal behavior, cybersuicide, web mining, machine learning, deep learning, recurrent neural networks
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.
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Technical information
Received: 27-09-2021
Revised: 24-10-2021
Accepted: 03-12-2021
OnlineFirst: 01-02-2022
Publication date: 01-04-2022
Article revision time: 27 days | Average time revision issue 71: 45 days
Article acceptance time: 67 days | Average time of acceptance issue 71: 70 days
Preprint editing time: 141 days | Average editing time preprint issue 71: 144 days
Article editing time: 186 days | Average editing time issue 71: 189 days
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La investigación educativa como didáctica en la enseñanza de la posverdad. Análisis de contextos AN Masegosa - Revista de Propuestas Educativas, 2023 - propuestaseducativas.org
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Castillo-Zúñiga, I., Luna-Rosas, F., & López-Veyna, J. (2022). Detection of traits in students with suicidal tendencies on Internet applying Web Mining. [Detección de rasgos en estudiantes con tendencia suicida en Internet aplicando Minería Web]. Comunicar, 71, 105-117. https://doi.org/10.3916/C71-2022-08