ProTect:一种混合深度学习模型,用于在社交媒体上主动检测网络欺凌
T Nitya Harshitha1, M Prabu1, E Suganya2
1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India.
Frontiers in artificial intelligence
|March 21, 2024
概括
本研究介绍了一种混合随机森林-CNN模型,用于在社交媒体上有效检测网络欺凌. 这种新的方法实现了96%的准确性,优于实时数据分析的标准模型.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
背景情况:
- 网络欺凌在社交媒体平台上是一个日益严重的问题.
- 现有的机器学习和深度学习模型与杂,不平衡的实时数据作斗争.
研究的目的:
- 为动态的,实时的社交媒体数据开发一个强大的网络欺凌检测模型.
- 提高网络欺凌检测的准确性和速度.
主要方法:
- 开发了一个基于森林的混合随机卷积神经网络 (CNN) 模型.
- 来自Twitter和Instagram的实时数据集被收集和注释.
- 建议模型的性能与各种机器学习 (ML) 和深度学习 (DL) 算法进行了比较.
主要成果:
- 混合随机森林-CNN模型在网络欺凌检测中实现了96%的准确性.
- 该模型与标准ML和DL算法相比显示出更高的性能.
- 结果比标准CNN模型快3.4秒,可以及时干预.
结论:
- 拟议的混合模型对于在社交媒体上实时检测网络欺凌是有效的.
- 这种方法为保护受害者和减轻在线骚扰带来了重大进展.
- 该模型的效率对于在动态的在线环境中快速响应至关重要.
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