Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence
Cyberbullying, which manifests in various forms, is a growing challenge on social media, mainly when it involves threats of violence through images, especially those featuring weapons. This study introduces a computational framework to identify such content using convolutional neural networks of wea...
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MDPI AG
2025-01-01
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| author | Liliana Ibeth Barbosa-Santillán Bertha Patricia Guzman-Velazquez Ma. Teresa Orozco-Aguilera Leticia Flores-Pulido |
| author_facet | Liliana Ibeth Barbosa-Santillán Bertha Patricia Guzman-Velazquez Ma. Teresa Orozco-Aguilera Leticia Flores-Pulido |
| author_sort | Liliana Ibeth Barbosa-Santillán |
| collection | DOAJ |
| description | Cyberbullying, which manifests in various forms, is a growing challenge on social media, mainly when it involves threats of violence through images, especially those featuring weapons. This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. By integrating artificial intelligence techniques with image analysis, our model detects visual patterns associated with violent threats, creating safer digital environments. The development of this work involved analyzing images depicting scenes with weapons carried by children or adolescents. Images were sourced from social media and spatial repositories. The statistics were processed through a 225-layer convolutional neural network, achieving an 86% accuracy rate in detecting weapons in images featuring children, adolescents, and young adults. The classifier method reached an accuracy of 17.86% with training over only 25 epochs and a recall of 14.2%. Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. Segmentation issues and the presence of background objects or people further compound this complexity. Our study demonstrates that convolutional neural networks can effectively detect weapons in images, making them a valuable tool in addressing cyberbullying involving weapon imagery. Detecting such content contributes to creating safer digital environments for young people. |
| format | Article |
| id | doaj-art-e739b5bcd6f5471ab1a29eb5ced64abb |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e739b5bcd6f5471ab1a29eb5ced64abb2025-08-20T03:12:19ZengMDPI AGInformation2078-24892025-01-011628010.3390/info16020080Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial IntelligenceLiliana Ibeth Barbosa-Santillán0Bertha Patricia Guzman-Velazquez1Ma. Teresa Orozco-Aguilera2Leticia Flores-Pulido3Departamento de Sistemas, Universidad de Guadalajara, Guadalajara 44100, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla 72840, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla 72840, MexicoFacultad de Ciencias Básicas, Ingeniería y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, MexicoCyberbullying, which manifests in various forms, is a growing challenge on social media, mainly when it involves threats of violence through images, especially those featuring weapons. This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. By integrating artificial intelligence techniques with image analysis, our model detects visual patterns associated with violent threats, creating safer digital environments. The development of this work involved analyzing images depicting scenes with weapons carried by children or adolescents. Images were sourced from social media and spatial repositories. The statistics were processed through a 225-layer convolutional neural network, achieving an 86% accuracy rate in detecting weapons in images featuring children, adolescents, and young adults. The classifier method reached an accuracy of 17.86% with training over only 25 epochs and a recall of 14.2%. Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. Segmentation issues and the presence of background objects or people further compound this complexity. Our study demonstrates that convolutional neural networks can effectively detect weapons in images, making them a valuable tool in addressing cyberbullying involving weapon imagery. Detecting such content contributes to creating safer digital environments for young people.https://www.mdpi.com/2078-2489/16/2/80cyberbullyingCNNdeeplearning |
| spellingShingle | Liliana Ibeth Barbosa-Santillán Bertha Patricia Guzman-Velazquez Ma. Teresa Orozco-Aguilera Leticia Flores-Pulido Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence Information cyberbullying CNN deeplearning |
| title | Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence |
| title_full | Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence |
| title_fullStr | Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence |
| title_full_unstemmed | Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence |
| title_short | Unraveling Cyberbullying Dynamis: A Computational Framework Empowered by Artificial Intelligence |
| title_sort | unraveling cyberbullying dynamis a computational framework empowered by artificial intelligence |
| topic | cyberbullying CNN deeplearning |
| url | https://www.mdpi.com/2078-2489/16/2/80 |
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