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

 Iván Castillo-Zúñiga, Aguascalientes (Mexico)

 Francisco-Javier Luna-Rosas, Aguascalientes (Mexico)

 Jaime-Iván López-Veyna, Zacatecas (Mexico)

https://doi.org/10.3916/C71-2022-08

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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Fundref

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Crossmark

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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