利用机器学习方法预防和分类网络欺凌事件的自动方法

Sheetal J, P Vinay Kumar, Vishal Raj, Vishwa Teja
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引用次数: 0

摘要

随着技术的进步和社交网络平台的日益普及,网络用户之间的个人信息共享已变得十分普遍。这种分享通过电脑和手机等各种设备毫不费力地实现。网络欺凌可以通过短信、文本消息和各种应用程序,以及社交媒体和论坛等在线平台表现出来,个人可以在这些平台上查看、参与或发布内容。该项目结合相关著作中报告的一系列方法,提供了对网络欺凌事件及其相应罪行的全面了解。该项目的实施为系统地结合各种要素或网络欺凌特征提供了机会。此外,还提出了网络欺凌相关罪行的综合清单。这些罪行在深度神经网络分类系统中根据特定标准进行排序,以帮助更好地分类和关联各自的事件。这有助于全面了解重复和潜在的犯罪活动。本研究的重点是通过声誉分数将用户帖子和图片内容分为欺凌和非欺凌两类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automatic Method to Prevent and Classify Cyberbullying Incidents using Machine Learning Approach
The technological advancements and the increasing popularity of social networking platforms, the sharing of personal information among online users has become widespread. This sharing occurs effortlessly through various devices such as computers and mobile phones. Cyberbullying can manifest through SMS, text messages, and various applications, as well as online platforms like social media and forums, where individuals can view, engage with, or distribute content.The project offers a comprehensive understanding of Cyberbullying incidents and their corresponding offences combining a series of approaches reported in relevant Work. The implementation provides the opportunity to systematically combine various element or Cyberbullying characteristics. Additionally, a comprehensive list of Cyberbullying-related offences is put forward. The offenses are ordered in a Deep Neural Network classification system based on specific criteria to assist in better classification and correlation of their respective incidents. This enables a thorough understanding of the repeating and underlying criminal activities. This study focuses on classifying user posts and image content into bullying or non bullying through reputation score
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