Open Access

ARTICLE

A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

Ali Alqazzaz¹, Mohammad Tabrez Quasim^1,*, Mohammed Mujib Alshahrani¹, Ibrahim Alrashdi², Mohammad Ayoub Khan¹

1 College of Computing and Information Technology, University of Bisha, 67714, Bisha, Saudi Arabia
2 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 72388, Saudi Arabia

* Corresponding Author: Mohammad Tabrez Quasim. Email:

Computer Systems Science and Engineering 2023, 46(1), 551-562. https://doi.org/10.32604/csse.2023.031048

Received 08 April 2022; Accepted 04 November 2022; Issue published 20 January 2023

Abstract

Facebook, Twitter, Instagram, and other social media have emerged as excellent platforms for interacting with friends and expressing thoughts, posts, comments, images, and videos that express moods, sentiments, and feelings. With this, it has become possible to examine user thoughts and feelings in social network data to better understand their perspectives and attitudes. However, the analysis of depression based on social media has gained widespread acceptance worldwide, other verticals still have yet to be discovered. The depression analysis uses Twitter data from a publicly available web source in this work. To assess the accuracy of depression detection, long-short-term memory (LSTM) and convolution neural network (CNN) techniques were used. This method is both efficient and scalable. The simulation results have shown an accuracy of 86.23%, which is reasonable compared to existing methods.

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Keywords

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