在社交媒体网络上早期检测网络欺凌的网站不可知论方法
Manuel López-Vizcaíno1, Francisco J Nóvoa1, Thierry Artieres2
1CITIC Research Center, Computer Science and Information Technologies Department, Campus de Elviña, 15071 A Coruña, Spain.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
早期发现网络欺凌至关重要. 使用Doc2Vec功能显著改善了早期检测模型,同时多个实例学习提高了Vine数据集的性能.
科学领域:
- 计算机科学 计算机科学
- 社交计算社会计算
- 自然语言处理自然语言处理.
背景情况:
- 网络欺凌的发生率随着社交媒体的使用而增加.
- 早期发现网络欺凌对于减轻社交媒体平台的负面影响至关重要.
研究的目的:
- 仅使用用户评论来调查网络欺凌的早期检测.
- 评估Doc2Vec功能和多个实例学习 (MIL) 在改进早期检测模型方面的有效性.
主要方法:
- 在两个独立的数据集上进行了实验:Instagram和Vine.
- 基线早期检测模型 (固定,门,双) 通过Doc2Vec功能得到了增强.
- 多重实例学习 (MIL) 应用于早期检测模型.
- 时间感知精度 (TaP) 被用作主要评估指标.
主要成果:
- Doc2Vec的特点是改善了基线早期检测模型的性能,达到79.6%.
- 多个实例学习 (MIL) 为Vine数据集提供了高达13%的额外改进.
- 在Instagram数据集上,MIL没有显示显著的性能提升.
结论:
- 在提升早期网络欺凌检测模型方面,Doc2Vec功能是有效的.
- 在数据集中,MIL显示了网络欺凌检测的希望,这些数据集具有较小的帖子大小和较少的英语使用,例如Vine.
- 可能需要进一步的研究来优化MIL的各种社交媒体平台,如Instagram.
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